DEVELOPMENT OF A PARTIAL SUPERVISION STRATEGY TO AUGMENT A NEAREST NEIGHBOUR CLUSTERING ALGORITHM FOR BIOMEDICAL DATA CLASSIFICATION

Sameh A. Salem, Nancy M. Salem and Asoke K. Nandi

In this paper, a partial supervision strategy for a recently developed clustering algorithm NNCA (Salem et al., 2006), Nearest Neighbour Clustering Algorithm, is proposed. The proposed method (NNCA-PS) offers classification capability with smaller amount of a priori knowledge, where a small number of data objects from the entire dataset are used as labelled objects to guide the clustering process towards a better search space. Results from the proposed supervision method indicate its robustness in classification compared with other classifiers.

A REGION BASED METHODOLOGY FOR FACIAL EXPRESSION RECOGNITION

Anastasios C. Koutlas and Dimitrios I. Fotiadis

Facial expression recognition is an active research field which accommodates the need of interaction between humans and machines in a broad field of subjects. This work investigates the performance of a multi-scale and multi-orientation Gabor Filter Bank constructed in such a way to avoid redundant information. A region based approach is employed using different neighbourhood size at the locations of 34 fiducial points. Furthermore, a reduced set of 19 fiducial points is used to model the face geometry. The use of Principal Component Analysis (PCA) is evaluated. The proposed methodology is evaluated for the classification of the 6 basic emotions proposed by Ekman considering neutral expression as the seventh emotion.

BIOSIGNAL-BASED COMPUTING BY AHL INDUCED SYNTHETIC GENE REGULATORY NETWORKS - From an in vivo Flip-Flop Implementation to Programmable Computing Agents

T. Hinze, T. Lenser, N. Matsumaru, P. Dittrich and S. Hayat

Gene regulatory networks (GRNs) form naturally predefined and optimised computational units envisioned to act as biohardware able to solve hard computational problems efficiently. This interplay of GRNs via signalling pathways allows the consideration as well as implementation of interconnection-free and fault tolerant programmable computing agents. It has been quantitatively shown in an in vivo study that a reporter gene encoding the green fluorescent protein (gfp) can be switched between high and low expression states, thus mimicking a NAND gate and a RS flip-flop. This was accomplished by incorporating the N-acyl homoserine lactone (AHL) sensing lux operon from Vibrio fischeri along with a toggle switch in Escherichia coli. gfp expression was quantified using flow cytometry. The computational capacity of this approach is extendable by coupling several logic gates and flip-flops. We demonstrate its feasibility by designing a finite automaton capable of solving a knapsack problem instance.

IMAGE SEGMENTATION TO EVALUATE ISLETS OF LANGHERANS

C. Grimaudo, D. Tegolo, C. Valenti and F. Bertuzzi

This contribution deals with an unsupervised system to process digital photomicrographs in order to locate and analyze islets of Langherans in human pancreases. The experiment has been conducted on real data and, though we are still going to complete the evaluation of the whole method, we expect to define a set of proper features (e.g. area, perimeter, fractal dimension, shape complexity, texture and entropy) useful for a fast and reliable counting of healthy cells. In particular, this research aims to measure the advisability of a possible implantation in patients affected by type 1 diabetes mellitus.

TRADITIONAL AVERAGING, WEIGHTED AVERAGING, AND ERPSUB FOR ERP DENOISING IN EEG DATA - A Comparison of the Convergence Properties

Andriy Ivannikov, Tommi Kärkkäinen, Tapani Ristaniemi and Heikki Lyytinen

In this article we compare the convergence rates of the three methods applied in ElectroEncephaloGraphy research for ERP denoising: traditional averaging, weighted averaging and ERPSUB. We derive the weighted averaging procedure based on maximizing SNR and show thereby that SNR criterion is equivalent to the originally proposed mean-square error criterion in the sense of the weighted averaging problem solving. Moreover, in order to characterize fully the performance of the selected methods we compare also noise reduction rates.

NOISE REDUCTION AND VOICE SEPARATION ALGORITHMS APPLIED TOWOLF POPULATION COUNTING

B. Dugnol, C. Fernández, G. Galiano and J. Velasco

We use signal and image theory based algorithms to produce estimations of the number of wolves emitting howls or barks in a given field recording as an individuals counting alternative to the traditional trace collecting methodologies. We proceed in two steps. Firstly, we clean and enhance the signal by using PDE based image processing algorithms applied to the signal spectrogram. Secondly, assuming that the wolves chorus may be modelled as an addition of nonlinear chirps, we use the quadratic energy distribution corresponding to the Chirplet Transform of the signal to produce estimates of the corresponding instantaneous frequencies, chirp-rates and amplitudes at each instant of the recording. We finally establish suitable criteria to decide how such estimates are connected in time.

BIOMIMETICS AND PROPORTIONAL NOISE IN MOTOR CONTROL

Christopher M. Harris

Proportional noise, in which the standard deviation of signal noise is proportional to signal mean, is a fundamental constraint on human motor performance but why it occurs is unknown. We show that for neural networks with binary thresholded units, channel capacity is maximised with a recruitment strategy that produces PN. The size principle also emerges, in agreement with observation. We therefore argue that Fitt’s law, speed-accuracy trade-off, and the minimum variance trajectories (including minimum jerk trajectories for limiting brief movements), which are observed in most human point-to-point movements, have evolved as optimal strategies resulting from maximising channel capacity. We conclude that biomimicry of minimum variance and minimum jerk trajectories in robotics is probably only of aesthetic value when using standard technology. In contrast, biomimicry using emergent neuromorphic technology in which networks are built from stochastic silicon ‘neurons’ with thresholds, is functional biomimetics and optimization of channel capacity will produce behaviours that are human-like.

A VOCAL TRACT VISUALISATION TOOL FOR A COMPUTER-BASED SPEECH TRAINING AID FOR HEARING-IMPAIRED INDIVIDUALS

Abdulhussain E. Mahdi

This paper describes a computer-based software tool for visualisation of the vocal-tract, during speech articulation, by means of a mid-sagittal view of the human head. The vocal tract graphics are generated by estimating both the area functions and the formant frequencies from the acoustic speech signal. First, it is assumed that the speech production process is an autoregressive model. Using a linear prediction analysis, the vocal tract area functions and the first three formants are estimated. The estimated area functions are then mapped to corresponding mid-sagittal distances and displayed as 2D vocal tract lateral graphics. The mapping process is based on a simple numerical algorithm and an accurate reference grid derived from x-rays for the pronunciation of a number English vowels uttered by different speakers. To compensate for possible errors in the estimated area functions due to variation in vocal tract length between speakers, the first two sectional distances are determined by the three formants. Experimental results show high correlation with x-ray data and the PARAFAC analysis. The tool also displays other speech parameters that are closely related to the production of intelligible speech and hence would be useful as a visual feedback aid for speech training of hearing–impaired individuals.

IDENTIFICATION OF HAND MOVEMENTS BASED ON MMG AND EMG SIGNALS

Pawel Prociow, Andrzej Wolczowski, Tito G. Amaral, Octávio P. Dias and Joaquim Filipe

This paper proposes a methodology that analysis and classifies the EMG and MMG signals using neural networks to control prosthetic members. Finger motions discrimination is the key problem in this study. Thus the emphasis is put on myoelectric signal processing approaches in this paper. The EMG and MMG signals classification system was established using the LVQ neural network. The experimental results show a promising performance in classification of motions based on both EMG and MMG patterns.

BIO-INSPIRED DATA AND SIGNALS CELLULAR SYSTEMS

André Stauffer, Daniel Mange and Joël Rossier

Living organisms are endowed with three structural principles: multicellular architecture, cellular division, and cellular differentiation. Implemented in digital according to these principles, our data and signals cellular systems present self-organizing mechanisms like configuration, cloning, cicatrization, and regeneration. These mechanisms are made of simple processes such as growth, load, branching, repair, reset, and kill. The data processed in the self-organizing mechanisms and the signals triggering their underlying processes constitute the core of this paper.

APPLICATION OF WALSH TRANSFORM BASED METHOD ON TRACHEAL BREATH SOUND SIGNAL SEGEMENTATION

Jin Feng, Farook Sattar and Moe Pwint

This paper proposes a robust segmentation method for differentiating consecutive inspiratory/expiratory episodes of different types of tracheal breath sounds. This has been done by applying minimal Walsh basis functions to transform the original input respiratory sound signals. Decision module is then applied to differentiate transformed signal into respiration segments and gap segments. The segmentation results are improved through a refinement scheme by new evaluation algorithm which is based on the duration of the segment. The results of the experiments, which have been carried out on various types of tracheal breath sounds, show the robustness and effectiveness of the proposed segmentation method.

A NEW METHOD FOR DETECTION OF BRAIN STEM IN TRANSCRANIAL ULTRASOUND IMAGES

Josef Schreiber, Eduard Sojka, Lacezar Licev, Petra Sknourilova, David Skoloudik and Jan Gaura

Transcranial sonography is to date only method able to detect structural damage of brain tissue in Parkinson’s disease patients. The problem is that the images provided by this method often suffer from a very poor quality what makes the final diagnosis strongly dependent on experience of examinating medical doctor. Our objective is to create a method that should help to minimize the physician’s subjectivity in the final diagnosis and should provide more exact information about the processed ultrasound images. The method itself is divided into two phases. In a first one, we try to locate the position of a minimal window, containing the brain stem, in an analyzed image. In a second phase, we locate and measure the echogenic substantia nigra area.

ANALYSIS OF DIFFERENCES BETWEEN SPECT IMAGES OF THE LEFT AND RIGHT CEREBRAL HEMISPHERES IN PATIENTS WITH EPILEPTIC SYMPTOMS

Elżbieta Olejarczyk and Małgorzata Przytulska

The aim of his work was examination of asymmetries in activity of the left and right cerebral hemispheres as well as localization and contouring of the regions of reduced or increased activity on the basis of single photon emission computer tomography (SPECT) images. The mean and standard deviation of normalized intensities inside the contoured areas of images, entropy based on intensity histograms and Chen’s fractal dimension were calculated.

A NEW METHOD FOR ICG CHARACTERISTIC POINT DETECTION

Maria Rizzi, Matteo D'Aloia and Beniamino Castagnolo

Impedance Cardiography is a cost-effective, non-invasive technique particularly useful in measuring cardiac functions. It evaluates systolic time intervals and stroke volume measuring thorax bioimpedance. In this paper, adopting the time-frequency analysis method, a new design has been developed to study the first derivative of impedance cardiography signal. The application of parallel wavelet filter banks has been investigated and a new method for ICG signal characteristic point detection has been developed. Test results show the improvement of the method in sensitivity and the feasibility of an easy implementation by design tools. Moreover, the algorithm noise immunity has been investigated.

MOTION ESTIMATION IN MEDICAL IMAGE SEQUENCES USING INVERSE POLYNOMIAL INTERPOLATION

Saleh Al-Takrouri and Andrey Savkin

In this paper, we propose a new method for motion estimation between two successive frames in medical image sequences and videos where the problem is defined in terms of pixel correspondence. The method is based on solving the problem of inverse polynomial interpolation and the solution is presented in the form of an iterative formula that numerically estimates the horizontal and vertical displacements of pixels between the two images. Examples are provided to show the performance of the proposed method.

PHASE SEGMENTATION OF NOISY RESPIRATORY SOUND SIGNALS USING GENETIC APPROACH

Feng Jin, Farook Sattar and Moe Pwint

In this paper, a new approach to automatically segment noisy respiratory sound signals is proposed. Segmentation is formulated as an optimization problem and the boundaries of the signal segments are detected using a genetic algorithm (GA). As the estimated number of segments present in a segmenting signal is initially obtained, a multi-population GA is employed to determine the locations of segment boundaries. The segmentation results are found through the generations of GA by introducing a new evaluation function, which is based on the sample entropy and a heterogeneity measure. Illustrative results for respiratory sound signals contaminated by loud heartbeats and other high level noises show that the proposed genetic segmentation method is quite accurate and threshold independent to find the noisy respiratory segments as well as the pause segments under different noisy conditions.

EFFECTIVENESS FOR A SLEEPINESS TEST OF PUPIL SIZE ESTIMATION DURING BLINK

Minoru Nakayama, Keiko Yamamoto and Fumio Kobayashi

Pupillary response has been used for an index of sleepiness, but the validity of the index is not clear. In this paper, the influence of blinks on the Pupillary Unrest Index (PUI) and the Power Spectrum Density (PSD) for the frequency range $f<0.8Hz$, as indices of pupil's instability during a sleepiness test, was examined. To estimate pupil size during blink, a procedure for collecting the clinical data was developed using Support Vector Regression (SVR). The values of PUI increased with experimental time, and the values and deviations of PUI for experimental observation were larger than the ones with SVR estimation. The blink time also increased with experimental time, and there were significant correlation relationships between the value of PUI and blink time. The mean PSD also correlated significantly with blink time. The relationship between pupillary indices and a subjective sleepiness index was not significant, as it was not in other previous works. These results provide evidence that pupillary indices were significantly affected by blink, and they did not reflect sleepiness correctly.

AUTOMATIC SEGMENTATION OF CAPILLARY NON-PERFUSION IN RETINAL ANGIOGRAMS

Amit Agarwal, Jayanthi Sivaswamy, Alka Rani and Taraprasad Das

Capillary Non-Perfusion (CNP) is a condition in diabetic retinopathy where blood ceases to flow to certain parts of the retina, potentially leading to blindness. This paper presents a solution for automatically detecting and segmenting CNP regions from fundus fluorescein angiograms (FFAs). CNPs are modeled as valleys, and a novel multi resolution technique for trough-based valley detection is presented. The proposed algorithm has been tested on 40 images and validated against expert-marked ground truth. Obtained results are presented as a receiver operating characteristic (ROC) curve. The area under this curve is 0.842 and the distance of ROC from the ideal point (0,1) is 0.31.

ECG SIGNAL DENOISING - Using Wavelet in Besov Spaces

Shi Zhao, Yiding Wang and Hong Yang

This paper proposes a novel technique to eliminate the noise in practical electrocardiogram (ECG) signals. Using wavelet bases to reduce the noise is a state-of-the-art denoising technique, which is first presented by Donoho and Johnstone. Traditional algorithms discuss wavelets in spaces. Compared to them, the proposed technique projects the ECG signals onto Besov spaces, which is a more sophisticated smoothness space, in order to determine the threshold of shrinkage function. In addition, instead of using linear shrinkage function, the proposed algorithm uses nonlinear hyper shrinkage function, which is proposed by S. Poornachandra. Combining the two techniques, we obtain a significant improvement over conventional wavelet denoising algorithm.

ELASTIC IMAGE WARPING USING A NEW RADIAL BASIC FUNCTION WITH COMPACT SUPPORT

Zhixiong Zhang and Xuan Yang

Thin plate spline (TPS) and compact support radial basis functions (CSRBF) are well-known and successful tools in medical image elastic registration base on landmark. TPS minimizes the bending energy of the whole image. However, in real application, such scheme would deform the image globally when deformation is local. Although CSRBF can limit the effect of the deformation locally, it cost more bending energy which means more information was lost. A new radial basic function named ‘Compact Support Thin Plate Spline Radial Basic Function’ (CSTPF) has been proposed in this paper. It costs less bending energy than CSRBF in deforming image locally and its global deformation effect is similar to TPS. Numerous experimental results show that CSTPF performs outstanding in both global and local image deformation.

TWO-STAGE CLUSTERING OF A HUMAN BRAIN TUMOUR DATASET USING MANIFOLD LEARNING MODELS

Raúl Cruz-Barbosa and Alfredo Vellido

This paper analyzes, through clustering and visualization, Magnetic Resonance spectra corresponding to a complex multi-center human brain tumour dataset. Clustering is performed as a two-stage process, in which the models used in the first stage are variants of Generative Topographic Mapping (GTM), belonging to the Manifold Learning family. In semi-supervised settings, class information can be added to refine the clustering process. Class information-enriched variants of GTM are used in this study to obtain a primary cluster description of the data. The number of clusters used by GTM is usually large for visualization purposes and does not necessarily correspond to the overall class structure. Consequently, in a second stage, clusters are agglomerated using the K-means algorithm with different initialization strategies, some of them defined ad hoc for the GTM models. We aim to evaluate whether the use of class information influences brain tumour cluster-wise class separability in the final result of the two-stage clustering process and under what circumstances this may be the case. We also explore the existence of atypical cases in the dataset and resort to a robust variant of GTM that detects outliers while effectively minimizing their negative impact in the clustering process.

TREMOR CHARACTERIZATION - Algorithms for the Study of Tremor Time Series

E. Rocon, A. F. Ruiz, J. C. Moreno, J. L. Pons, J. A. Miranda and A. Barrientos

A great deal of effort has been devoted in the past decades in the generic area of tremor management. Specific topics of modelling for objective classification of pathological tremor out of kinematics and physiological data, compensatory technologies and evaluation rating tools are just a few examples of application field. This paper introduces the work developed by the authors in the study of tremor time series. First, it introduces a novel technique for the study of tremor. The technique presented is a high-resolution technique that solves most of limitations of the Fourier Analysis (the standard technique to the study of tremor time series). This technique was used for the study of tremorous movement in joints of the upper limb. After, some conclusions about tremor behaviour in upper limb based on the technique introduces are presented. Furthermore, an algorithm able to estimated in real-time the voluntary and the tremorous movement was presented. This algorithm was validated in two contexts with successful results. Finally, some conclusions and future work are given.

ACOUSTIC INDICES OF CARDIAC FUNCTIONALITY

Guy Amit, Jonathan Lessick, Noam Gavriely and Nathan Intrator

The mechanical processes of the cardiac cycle generate vibratory and acoustic signals that are received on the chest wall. We describe signal processing and feature extraction methods utilizing these signals for continuous non-invasive monitoring of systolic cardiac functionality. Vibro-acoustic heart signals were acquired from eleven subjects during a routine pharmacological stress echocardiography test. Principal component analysis, applied to the joint time-frequency distribution of the first heart sound (S1), revealed a pattern of an increase in the spectral energy and the frequency bandwidth of the signal associated with the increase of cardiac contractility during the stress test. Novel acoustic indexes of S1 that compactly describe this pattern showed good linear correlation with reference indexes of systolic functionality estimated by strain-echocardiography. The acoustic indexes may therefore be used to improve monitoring and diagnosis of cardiac systolic dysfunction.

ANALYSIS OF FOCUSES OF ATTENTION DISTRIBUTION FOR A NOVEL FACE RECOGNITION SYSTEM

C. Spampinato, M. Nicotra and A. Travaglianti

In this paper we propose an automated approach to recognize human faces based on the analysis of the distribution of the focuses of attention (FOAs) that reproduces the ability of the humans in the interpretation of visual scenes. The analysis of the FOAs (distribution and position), carried out by an efficient and source light independent visual attention module, allows us to integrate the face features (e.g., eyes, nose, mouth shape) and the holistic features (the relations between the various parts of the face). Moreover, a remarkable approach has been developed for skin recognition based on the shifting of the Hue plane in the HSL color space.

REGISTRATION AND RETRIEVAL OF ELONGATED STRUCTURES IN MEDICAL IMAGES

Alexei Manso Correa Machado and Christiano Augusto Caldas Teixeira

This work aims at proposing a set of methods to describe, register and retrieve images of elongated structures from a database based on their shape content. We propose a registration algorithm that jointly takes into account the gross shape of the structure and the shape of its boundary, resulting in anatomically consistent deformations. The method determines a medial axis that represents the full extent of the structure with no branches. Registration follows the linear elasticity model and is implemented through dynamic programming. Discriminative anatomic features are computed from the results of registration and used as variables in a content-based image retrieval system. A case study on the morphology of the corpus callosum in the chromosome 22q11.2 deletion syndrome illustrates the effectiveness of the method and corroborates the hypothesis that retrieval systems may also act as knowledge discovery tools.

NONLINEAR MODELING OF CARDIOVASCULAR RESPONSE TO EXERCISE

Lu Wang, Steven W. Su, Gregory S. H. Chan, Branko G. Celler, Teddy M. Cheng and Andrey V. Savkin

This study experimentally investigates the relationships between central cardiovascular variables and oxygen uptake based on nonlinear analysis and modeling. Ten healthy subjects were studied using cycle-ergometry exercise tests with constant workloads ranging from 25 Watt to 125 Watt. Breath by breath gas exchange, heart rate, cardiac output, stroke volume and blood pressure were measured at each stage. The modeling results proved that the nonlinear modeling method (Support Vector Regression) outperforms traditional regression method (reducing Estimation Error between 59% and 80%, reducing Testing Error between 53% and 72%) and is the ideal approach in the modeling of physiological data, especially with small training data set.

NONLINEAR MODELLING AND CONTROL OF HEART RATE RESPONSE TO TREADMILLWALKING EXERCISE

Teddy M. Cheng, Andrey V. Savkin, Branko G. Celler, Steven W. Su and Lu Wnag

In this study, a nonlinear system was developed for the modelling of the heart rate response to treadmill walking exercise. The model is a feedback interconnected system which can represent the neural response and peripheral local response to exercise. The parameters of the model were identified from an experimental study which involved 6 healthy adult male subjects, each completed 3 sets of walking exercise at different speeds. The proposed model will be useful in explaining the cardiovascular response to exercise. Based on the model, a 2-degree-of-freedom controller was developed for the regulation of the heart rate response during exercise. The controller consists of a piecewise LQ and $H_{\infty}$ controllers. Simulation results showed that the proposed controller had the ability to regulate heart rate at a given target, indicating that the controller can play an important role in the design of exercise protocols for individuals.

BREAST CANCER DETECTION USING GENETIC PROGRAMMING

Hong Guo, Qing Zhang and Asoke K. Nandi

Breast cancer diagnosis have been investigated by different machine learning methods. This paper proposes a new method for breast cancer diagnosis using a single feature generated by Genetic Programming(GP). GP as an evolutionary mechanism that provides a training structure to generate features. The presented approach is experimentally compared with some kernel feature extraction methods: The Kernel Principal Component Analysis (KPCA) and Kernel Generalised Discriminant Analysis (KGDA). Results demonstrate the capability of this method to transform information from high dimensional feature space into one dimensional space for breast cancer diagnosis.

BREAST CANCER DIAGNOSIS AND PROGNOSIS USING DIFFERENT KERNEL-BASED CLASSIFIERS

Tingting Mu and Asoke Nandi

The medical applications of several advanced, kernel-based, nonlinear classifiers to breast cancer diagnosis and prognosis are studied and compared in this paper. The pairwise Rayleigh quotient (PRQ) classifier and kernel Fisher’s discriminative analysis (KFDA) seek one discriminant boundary based on the scatter measurements. The support vector machines (SVMs) seek one discriminant boundary based on the maximal margin rule. The strict 2-surface proximal (S2SP) classifier and multisurface proximal SVMs (MPSVMs) learn two proximal hyperplanes by optimizing two Rayleigh quotients. The Radial basis function (RBF) kernel is employed to incorporate the nonlinearity. Studies are conducted with the Wisconsin diagnosis and prognosis breast cancer (WDBC and WPBC) datasets generated from fine needle aspiration (FNA) samples by image processing. Comparative analysis are developed on the classification accuracies, computing times, and sensitivities to regularization parameters for the above kernel-based classifiers.

AN EFFICIENT METHOD FOR VESSEL WIDTH MEASUREMENT ON COLOR RETINAL IMAGES

Alauddin Bhuiyan, Baikunth Nath, Joselito Chua and Kotagiri Ramamohanarao

Vessel diameter is an important factor for indicating retinal microvascular signs. In automated retinal image analysis, the measurement of vascular width is a complicated process as most of the vessels are few pixels wide. In this paper, we propose a new technique to measure the retinal blood vessel diameter which can be used to detect arteriolar narrowing, arteriovenous (AV) nicking, branching coefficients, etc. to diagnose related diseases. First, we apply the Adaptive Region Growing (ARG) segmentation technique to obtain the edges of the blood vessels. Following that we apply the unsupervised texture classification method to segment the blood vessels from where we obtain the vessel centreline. Then we utilize the edge image and vessel centreline image to obtain the potential pixels pairs which pass through a centreline pixel. We apply a rotational invariant mask to search the pixel pairs from the edge image. From those pixels we calculate the shortest distance pair which will be the vessel width for that cross-section. We evaluate our technique with manually measured width for different vessels' cross-sectional area which shows that our technique is very accurate.

MODEL ORDER ESTIMATION FOR INDEPENDENT COMPONENT ANALYSIS OF EPOCHED EEG SIGNALS

Peter Mondrup Rasmussen,Morten Mørup, Lars Kai Hansen and Sidse M. Arnfred

In analysis of multi-channel event related EEG signals indepedent component analysis (ICA) has become a widely used tool to attempt to separate the data into neural activity, physiological and non-physiological artifacts. High density elctrode systems offer an opportunity to estimate a corresponding large number of independent components (ICs). However, too large a number of ICs leads to overfitting of the ICA model, which can have a major impact on the model validity. Consequently, finding the optimal number of components in the ICA model is an important problem. In this paper we present a method for model order selection, based on a probabilistic framework. The proposed method is a modification of the Molgedey Schuster (MS) algorithm to epoched, i.e. event related data. Thus, the contribution of the present paper can be summarized as follows: 1) We advocate MS as a low complexity ICA alternative for EEG. 2) We define an epoch based likelihood function for estimation of a principled unbiased 'test error'. 3) Based on the unbiased test error measure we perform model order selection for ICA of EEG. Applied to a 64 channel EEG data set we were able to determine an optimum order of the ICA model and to extract 22 ICs related to the neurophysiological stimulus responses as well as ICs related to physiological- and non-physiological noise. Furthermore, highly relevant high frequency responce information was captured by the ICA model.

USE OF CEPSTRUM-BASED PARAMETERS FOR AUTOMATIC PATHOLOGY DETECTION ON SPEECH - Analysis of Performance and Theoretical Justification

Rubén Fraile, Juan Ignacio Godino-Llorente, Nicolás Sáenz-Lechón, Víctor Osma-Ruiz and Pedro Gómez-Vilda

The majority of speech signal analysis procedures for automatic pathology detection mostly rely on parameters extracted from time-domain processing. Moreover, calculation of these parameters often requires prior pitch period estimation; therefore, their validity heavily depends on the robustness of pitch detection. Within this paper, an alternative approach based on cepstral-domain processing is presented which has the advantage of not requiring pitch estimation, thus providing a gain in both simplicity and robustness. While the proposed scheme is similar to solutions based on Mel-frequency cepstral parameters, already present in literature, it has an easier physical interpretation while achieving similar performance standards.

BIOSIGNAL ACQUISITION DEVICE - A Novel Topology for Wearable Signal Acquisition Devices

Luca Maggi, Luca Piccini, Sergio Parini, Giuseppe Andreoni and Guido Panfili

The here presented work illustrates a novel circuit topology for the conditioning of biomedical signals. The system is composed of an amplification chain and relies on a double feedback path which assure the stability of the system whichever the amplification block gain and the order of the low-pass filter are. During the normal operation the offset recovery circuit has a linear transfer function, when it detects a saturation of the amplifier, it automatically switches to the fast recovery mode and restores the baseline in few milliseconds. The proposed configuration has been developed in order to make wearable biosignal acquisition devices more robust, simpler and smaller. Thanks to the used AC coupling method, very low high-pass cut-off frequencies, can be achieved even using small valued passive components with advantages in terms of circuit bulkiness. The noise rejection filter between the pre-amplification and the amplification stages eliminates the out-of-band noise before the amplification reducing the possibility of having clipping noise and minimizing the dynamic power consumption. The presented topology is currently used in a prototypal EEG acquisition device in a Brain Computer Interface (BCI) system, and in a commercial polygraph which will be soon certificated for clinical use.

MICROGLIA MODELLING AND ANALYSIS USING L-SYSTEMS GRAMMAR

Herbert F. Jelinek and Audrey Karperien

Medical image analysis requires in the first instance information on the extent of normal variation in a biological system in order to identify pathological changes. MicroMod is an L-system based modelling software package available through the World Wide Web that allows construction of branching structures such as neurons and glia. In addition MicroMod includes analystical software to analyse complex structures such as fractal analysis and lacunarity. MicroMod consists of three options with subroutines for constructing branching structures in a deterministic or probabilistic manner. The fractal dimensions of microglia visualised using histochmical techniques with modelled glia using MicroMod showed good agreement (1.423 and 1.425 respectively). An analysis of simulated microglia by fractal analysis indicates that changes in the length of sub-branches relative to the parent branch with the number of sprouts remaining the same and manipulating the scale of sub to parent branch diameter and the number of new branches per branch affected the fractal dimension and lacunarity. The results indicate that MicroMod provides a useful adjunct to neuroscience research and provides a platform for understanding complex changes in structure associated with normal function and disease processes.

STATISTICAL SIGNIFICANCE IN OMIC DATA ANALYSES - Alternative/Complementary Method for Efficient Automatic Identification of Statistically Significant Tests in High Throughput Biological Studies

Christine Nardini, Luca Benini and Michael D. Kuo

The post-Genomic Era is characterized by the proliferation of high-throughput platforms that allow the parallel study of a complete body of molecules in one single run of experiments (omic approach). Analysis and integration of omic data represent one of the most challenging frontiers for all the disciplines related to Systems Biology. From the computational perspective this requires, among others, the massive use of automated approaches in several steps of the complex analysis pipeline, often consisting of cascades of statistical tests. In this frame, the identification of statistical significance has been one of the early challenges in the handling of omic data and remains a critical step due to the multiple hypotheses testing issue, given the large number of hypotheses examined at one time. Two main approaches are currently used: p-values based on random permutation approaches and the False Discovery Rate. Both give meaningful and important results, however they suffer respectively from being computationally heavy -due to the large number of data that has to be generated-, or extremely flexible with respect to the definition of the significance threshold, leading to difficulties in standardization. We present here a complementary/alternative approach to these current ones and discuss performances and limitations.

PRINCIPAL COMPONENT ANALYSIS OF THE P-WAVE

Federica Censi, Giovanni Calcagnini, Pietro Bartolini, Chiara Ricci, Renato Pietro Ricci and Massimo Santini

Aim of this study is to perform the principal component analysis (PCA) of the P-wave in patients prone to atrial fibrillation (AF). Eighteen patients affected by paroxysmal AF and implanted with pacemakers were studied. Two 5-minute ECG recordings were performed: during spontaneous (SR) and paced rhythm (PR). ECG signals were acquired using a 32-lead system (2048 Hz, 24 bit, 0-400 Hz bandwidth). For each patient, PCA of the averaged P-waves extracted in any of the 32 leads has been performed. We computed PCA parameters related to the dipolar (using the first 3 PCs) and not dipolar (from the 4th to the 32nd PCs) components of the P-wave. The number of PCs according to the latent root criterion ranges between 2 and 3 during SR and between 2 and 4 during PR. PCA parameters related to the 3 largest PCs, and describing the dipolar component of the P-wave, did not significantly differ during SR and PR. The not dipolar components during SR were significantly lower than during PR (PCAres%: 0.03±0.06 vs 0.12±0.21, p=0.001; PCAres [mV4]: 0.10±0.14 vs 0.49±0.73, p=0.001). Factor analysis showed that on average all leads contributes to the first principal component. These findings encourage the use of PCA to obtain crucial quantitative information from surface ECG P-wave.

SPECTRAL AND CROSS-SPECTRAL ANALYSIS OF CONDUCTANCE CATHETER SIGNALS - New Indexes for Quantification of Mechanical Dyssinchrony

Sergio Valsecchi, Luigi Padeletti, Giovanni Battista Perego, Federica Censi, Pietro Bartolini and Jan J. Schreuder

We hereby present novel indexes to quantify ventricular mechanical dyssynchrony by using spectral and cross-spectral analysis of conductance catheter volume signals. Conductance catheter is a volume measurement technique based on conductance measurement: the intraventricular volume, i.e. the time-varying volume of blood contained within the heart cavity, is estimated by measuring the electrical conductance of the blood employing a multi-pole catheter. Five segmental volume signals (SVi, i=1,…5) can be acquired; total volume (TV) is estimated as the instantaneous sum of the segmental volumes. We implemented classical time-domain dyssynchrony indexes already utilized in conductance catheter signals analysis, and new frequency-domain indexes. Study population consisted of 15 heart failure (HF) patients with left bundle branch block and 12 patients with preserved left ventricular (LV) function. We found that spectral measures seem to out-perform classical time-domain parameters in differentiating atrial HF patients from no-HF group. These findings encourage the use of spectral analysis to obtain crucial quantitative information from conductance catheter signals.

EVOLUTIONARY COMPUTATION APPROACH TO ECG SIGNAL CLASSIFICATION

Farid Melgani and Yakoub Bazi

In this paper, we propose a novel classification system for ECG signals based on particle swarm optimization (PSO). The main objective of this system is to optimize the performance of the support vector machine (SVM) classifier in terms of accuracy by automatically: i) searching for the best subset of features where to carry out the classification task; and ii) solving the SVM model selection issue. Experiments conducted on the basis of ECG data from the MIT-BIH arrhythmia database to classify five kinds of abnormal waveforms and normal beats confirm the effectiveness of the proposed PSO-SVM classification system.

COMPARATIVE STUDY OF SEVERAL NOVEL ACOUSTIC FEATURES FOR SPEAKER RECOGNITION

Vladimir Pervouchine, Graham Leedham, Haishan Zhong, David Cho and Haizhou Li

Finding good features that represent speaker identity is an important problem in speaker recognition area. Recently a number of new and novel acoustic features have been proposed for speaker recognition. The researchers use different data sets and sometimes different classifiers to evaluate the features and compare them to the baselines such as MFCC or LPCC. However, due to different experimental conditions direct comparison of those features to each other is difficult or impossible. This paper presents a study of five new acoustic features recently proposed. The feature extraction has been performed on the same data (NIST~2001~SRE), and the same UBM-GMM classifier has been used. The results are presented as DET curves with equal error ratios indicated. Also, an SVM-based combination of GMM scores produced on different features has been made in hope that classifier fusion can result in higher speaker recognition accuracy. The results for different features as well as for their combinations are directly comparable to each other and to those obtained with the baseline MFCC features.

COMBINING NOVEL ACOUSTIC FEATURES USING SVM TO DETECT SPEAKER CHANGING POINTS

Haishan Zhong, David Cho, Vladimir Pervouchine and Graham Leedham

Automatic speaker change point detection segments different speakers from continuous speech according to speaker characteristics. This is often a necessary step before applying speaker verification or identification systems. Among the features to represent a speaker in the speaker change point detection systems acoustic features are commonly used. Commonly used features are Mel Frequency Cepstral Coefficients (MFCC) and Linear Prediction Cepstral Coefficients (LPCC). However, the features are affected by speech content, environment, type of recording device, etc. So far, no features have been discovered, which values depend only on the speaker. In this paper four novel feature types proposed in recent major journals and conference papers for speaker verification problem, are applied to the problem of speaker change point detection. The features are also used to form a combination scheme via SVM classifier. The results shows that the proposed scheme improves the performance of speaker changing point detection as compared to the system that uses MFCC features. It was also found that some of the novel features of low dimensionality give comparable speaker change point detection accuracy to the high-dimensional MFCC features.

POSSIBILITY OF MENTAL HEALTH SELF-CHECKS USING DIVERGENCE PROPERTIES OF PULSE WAVES

Mayumi Oyama-Higa and Tiejun Miao

We conducted a nonlinear analysis of fingertip pulse waves and found that the Lyapunov exponent referencing the “divergence” of attractor trajectory is an effective method for determining mental health in humans. In particular, we showed that this method is very effective for the early detection of dementia and depression, as well as in the detection of mental changes in healthy persons. In contrast, current measurement methods to determine mental health are subjective in most cases and are neither objective nor simple in terms of time and cost. The development of an apparatus allowing easy measurement for many users is therefore necessary. We illustrate the possibility of mental health self-checks using pulse wave divergence based on a series of examples in previous studies. In addition, we developed software to express the fluctuation of the Lyapunov exponent using time series data from multiple measurements. If changes in mental status can be assessed by studying the fluctuation factor of the Lyapunov exponent, we will be closer to effectively evaluating and controlling mental health problems. And, we developed an easy-to-use economical device, a PC mouse with an integrated sensor for measuring the pulse waves.

IDENTIFICATION OF TIME-VARYING T-WAVE ALTERNANS FROM 20-MINUTE ECG RECORDINGS - Issues Related to TWA Magnitude Threshold and Length of ECG Time Series

Laura Burattini, Wojciech Zareba and Roberto Burattini

Aim of this study was the assessment of a T-wave alternans (TWA) identification procedure based on application of an adaptive match filter (AMF) method, recently developed by ourselves, to a 20-minute digital ECG recording (ECG20). Three-lead ECG20 tracings from 20 patients who survived an acute myocardial infarction (AMI-group) and 20 healthy subjects (H-group) were analysed. The AMI-group showed, on average, increased levels of TWA (P<0.05). Considering that noise may cause false positive TWA detection, a threshold (THRTWA) was defined for TWA magnitude (TWAM) as the mean TWAM +2SD over the H-group. TWAM exceeding this threshold identified a TWA-positive subject (TWA+) as one at increased risk of sudden cardiac death. Eight (40%) AMI-patients vs. zero H-subjects were detected as TWA+. This result meets clinical expectation. TWA manifested as a non stationary phenomenon that could even be missed in all TWA+ subjects if our AMF (as well as any other technique) was applied to a single short-term 128-beat ECG series, as usually done in previous reports. In conclusion, our AMF-based TWA identification technique, applied to 20-minute ECG recordings, yields a good compromise between reliability of time-varying TWA identification and computational efforts.

NETWORK TOMOGRAPHY-BASED TRACKING FOR INTRACELLULAR TRAFFIC ANALYSIS IN FLUORESCENCE MICROSCOPY IMAGING

Thierry Pécot, Charles Kervrann and Patrick Bouthemy

Determination of the sub-cellular localization and dynamics of any proteins is an important step towards the understanding of multi-molecular complexes in a cellular context. Green Fluorescent Protein (GFP)-tagging and time-lapse fluorescence microscopy allows to acquire multidimensional data on rapid cellular activities, and then make possible the analysis of proteins of interest. Consequently, novel techniques of image analysis are needed to quantify dynamics of biological processes observed in such image sequences. In biological trafficking analysis, the previous tracking methods do not manage when many small and poorly distinguishable objects interact. Nevertheless, an another way of tracking that usually consists in determining the full trajectories of all the objects, can be more relevant. General information about the traffic like the regions of origin and destination of the moving objects represent interesting features for analysis. In this paper, we propose to estimate the paths (regions of origin and destination) used by the objects of interest, and the proportions of moving objects for each path. This can be accomplished by exploiting the recent advances in Network Tomography (NT) commonly used in network communications. This idea is demonstrated on real image sequences for the Rab6 protein, a GTPase involved in the regulation of intracellular membrane trafficking.

A HYBRID METHOD BASED ON FUZZY INFERENCE AND NON-LINEAR OSCILLATORS FOR REAL-TIME CONTROL OF GAIT

J. C. Moreno, J. L. Pons, E. Rocon and Y. Demiris

Robust generation of motor commands for real-time control of locomotion with artificial means is crucial for human safety. This paper addresses the combination of fuzzy inference for determination of rules with a non linear oscillator system, as generators of motor commands for the control of human leg joints during walking, by means of external gait compensators, e.g. exoskeletons, functional electrical stimulation or hybrid systems. The response of the proposed method is evaluated for variations in stride frequency and step length. The testing during gait conditions is performed considering inertial sensing as feedback in a simulation study. The reference data considered is obtained in multiple experiments with healthy subjects walking with a controllable exoskeleton designed to compensate quadriceps weakness. A model of the operation of the knee joint compensation provided by the exoskeleton is obtained as reference to evaluate the method based on real data. The results demonstrate the benefits of both incorporating a) the fuzzy inference system in cyclical decision making for generation of motor commands and b) the dynamic adaptation of the timing parameters of the external compensator provided by the van der Pol oscillator.

A IMAGE PROCESSING METHOD FOR COMPARISON OF MULTIPLE RADIOGRAPHS

Chen Sheng, Li Li and Wang Pei

Portable chest radiography is the most commonly ordered radiographic test in the intensive care unit (ICU). In the ICU, a succession of portable images is usually taken over a period of time to monitor the progress of a patient’s condition. A prompt diagnosis of any changes in the conditions of these ICU patients allows clinicians to provide immediate attention and treatments that are required to prevent the conditions from worsening and which could result in a treat to the patient’s life. However, because of differences in X-ray exposure setting, patient and apparatus positioning, scattering, and grid application, for example, differences in image quality from on image to the next taken at different times can be significant. The differences in image quality make it difficult for clinicians to compare images to detect subtle changes. This paper presents an image-rendering method that reduces the variability in image appearance and enhances the diagnostic quality of these images. Use of the presented method allows clinicians to detect subtle pathological changes from one image to the next, thus improving the quality of patient management in the ICU.

AUTOMATED DETECTION OF SUPPORTING DEVICE POSITIONING IN RADIOGRAPHY

Chen Sheng, Li Li and Ying Jun

Portable X-ray radiographs are heavily used in the ICU for detecting significant or unexpected conditions requiring immediate changes in patient management. One concern for effective patient management relates to the ability to detect the proper positioning of tubes that have been inserted into the patient. These include, for example, endo-tracheal tubes (ET), feeding tubes (FT), naso-gastric tubes (NT), and other tubes. Proper tube positioning can help to ensure delivery or disposal of liquids and air/gases to and from the patient during a treatment procedure. Improper tube positioning can cause patient discomfort, render a treatment ineffective, or can even be life-threatening. However, because the poor image quality in portable AP X-ray images due to the variability in patients, apparatus positioning, and X-ray exposure, it is often difficult for clinicians to visually detect the position of tube tips. Thus, there is a need for detecting and identifying tube position and type to assist clinicians. The purpose of this paper is to present a computer-aided method for automated detection of tubes and identification of tube types. Use of this method may allow clinicians to detect the tube tips more easily and accurately, thus improving the quality of patient management in the ICU.

INFLUENCES OF DIGITAL BAND-PASS FILTERING ON THE BCG WAVEFORM

Mikko Koivuluoma, Laurentiu Barna, Alpo Värri, Teemu Koivistoinen, Tiit Kööbi and Alpo Värri

Ballistocardiography is a non-invasive technique for the assessment of cardiac function. The BCG signals usually have two main components: the heart originated component and the respiratory originated component. The frequency bands of these components overlaps, and hereby complete separation of these two components is not possible. In this study, we used several band pass filters to remove the respiratory, and tried to estimate the optimal lower cut-off frequency for this band pass filter. The optimal band pass filter should have very small effect to the heart originated BCG. We found that the optimal lower cout-off frequency is about 1.3 Hz.

BALLISTOCARDIOGRAPHIC ARTIFACT REMOVAL FROM SIMULTANEOUS EEG/FMRI RECORDING BY MEANS OF CANONICAL CORRELATION ANALYSIS

S. Assecondi, P. Van Hese, H. Hallez, Y. D'Asseler, I. Lemahieu, A. M. Bianchi and P. Boon

The electroencephalogram (EEG) is a standard technique to record and study the brain activity with a high temporal resolution. Blood oxygenation level dependent functional magnetic resonance imaging (BOLD fMRI) is a non-invasive imaging method that allows the localization of activated brain regions with a high spatial resolution. The co-recording of these two complementary modalities can give new insights into how the brain functions. However, the interaction between the strong electromagnetic field (3T) of the MR scanner and the currents recorded by the electrodes placed on the scalp generates artifacts that obscure the EEG and diminish its readability. In this work we used canonical correlation analysis (CCA) in order to remove the ballistocardiographic artifact (BCGa). CCA is applied to two consecutive windows in order to take into account both spatial and temporal information. We showed that users can easily remove the artifact through a graphical user interface by adjusting the number of components to be removed according to visual inspection of the signal and its power spectrum.

ON-CHIP FLUORESCENCE LIFETIME EXTRACTION USING SYNCHRONOUS GATING SCHEME - Theoretical Error Analysis and Practical Implementation

Day-Uei Li, Bruce Rae, David Renshaw, Robert Henderson and Eleanor Bonnist

A synchronous gating technique was proposed for fluorescent photon collecting. The two-gate rapid lifetime determination (RLD) technique was applied to implement on-chip fluorescence lifetime extraction. Compared with all available iterative least square method (LSM) or maximum likelihood estimation (MLE) based general purpose FLIM analysis software, our chips offer direct calculation of lifetime based on the photon counts stored on the on-chip memory and deliver faster analysis for higher possibility of real-time applications, such as clinical diagnosis. The cost of our chips is much less than available solutions, since we don’t need any data fitting software and photon counting card. Theoretical error analysis of the two- and multi-gate RLDs were derived for comparison. And we applied a two-gate RLD scheme based on the analysis suggested. The performance of the chips were tested on a single-exponential Rhodamine B obtained from our SPAD detector using 468nm laser diode as light sources with optimized gate width. Moreover, a multi-exponential pipelined two-gate RLD (PL-RLD-2) FLIM was also proposed and tested on a four-exponential decays DNA sample containing a single adenine analogue 2-aminopurine.

MOUSE CONTROL THROUGH ELECTROMYOGRAPHY - Using Biosignals Towards New User Interface Paradigms

Vasco Vinhas and Antonio Gomes

Recent technologic breakthroughs have enabled the usage of minimal invasive biometric hardware devices that no longer interfere with the audience immersion feeling. The usage of EMG to extend traditional mouse-oriented user interfaces is a proof-of-concept prototype integrated in a wider horizon project. A subset of the main project's architecture was reused, specially the communication middleware, as a stable development platform. An originally intended EEG hardware was adapted to perform EMG and therefore detect muscular activity. It was chosen, as a practical proof-of-concept, that it was desired to detect winking as a triggering device to perform a given traditional user interface action. The described application achieved extremely positive records with hit rates of around 90%. The volume of false positives and undetected desired actions are considered negligible due to both system development stage and application contextualization – non critical systems. The success and acceptance levels of the project are really encouraging not only to the enhancement of the proposed application but also to the global system continuous development.

DO MOBILE PHONES AFFECT SLEEP? - Investigating Effects of Mobile Phone Exposure on Human Sleep EEG

Andrew Wood, Sarah Loughran, Rodney Croft, Con Stough and Bruce Thompson

This paper will summarize the results of a human volunteer study on the effects on sleep parameters of exposure to RF emissions from a mobile phone handset for 30min prior to going to sleep. A cohort of 55 volunteers were tested over 4 nights in a double-blind design. The significant outcomes were: Rapid Eye Movement (REM) sleep latency reduced by 16%; EEG alpha power enhanced by 8% during 1st non-REM period. These results are compared for overall internal consistency and with studies from other laboratories. Part of the program of the Australian Centre for Radiofrequency Bioeffects Research extending these studies is described.

A NOVEL TEMPLATE HUMAN FACE MODEL WITH TEXTURING

Ken Yano and Koichi Harada

We present a method to fit a template face model to 3D scan face. We first normalize the size and align the orientation then fit the model roughly by scattered interpolation method. Secondly we run the optimization method based on Allen's work. We are able to generate face models which have "poin-to-point" correspondence among them. We also suggest a way to transfer any facial texture image over this fitted model.

ANT COLONY INSPIRED METAHEURISTICS IN BIOLOGICAL SIGNAL PROCESSING - Hybrid Ant Colony and Evolutionary Approach

Miroslav Bursa, Michal Huptych and Lenka Lhotska

Nature inspired metaheuristics have interesting stochastic properties which make them suitable for use in data mining, data clustering and other application areas, because they often produce more robust solutions. This paper presents an application of clustering method inspired by the behavior of real ants in the nature in biomedical signal processing. The main aim of our study was to design and develop a combination of feature extraction and classification methods for automatic recognition of significant structure in biological signal recordings. The method would speed up and increase objectivity of identification of important classes and may be used for online classification and can be also used as a hint in the expert classification. We have obtained significant results in electrocardiogram and electroencephalogram recordings, which justify the use of such methods method.

ON THE FUTILITY OF INTERPRETING OVER-REPRESENTATION OF MOTIFS IN GENOMIC SEQUENCES AS FUNCTIONAL SIGNALS

Nikola Stojanovic

Locating signals for the initiation of gene expression in DNA sequences is an important unsolved problem in genetics. Over more than two decades researchers have applied a large variety of sophisticated computational techniques in order to address it, but only with moderate success. In this paper we investigate the reasons for the relatively poor performance of the current models, and outline some possible directions for future work in this field.

INVESTIGATION OF ICA ALGORITHMS FOR FEATURE EXTRACTION OF EEG SIGNALS IN DISCRIMINATION OF ALZHEIMER DISEASE

Jordi Solé-Casals, François Vialatte, Zhe Chen and Andrzej Cichocki

In this paper we present a quantitative comparisons of different independent component analysis (ICA) algorithms in order to investigate their potential use in preprocessing (such as noise reduction and feature extraction) the electroencephalogram (EEG) data for early detection of Alzhemier disease (AD) or discrimination between AD (or mild cognitive impairment, MCI) and age-match control subjects.

USING WAVELET TRANSFORM FOR FEATURE EXTRACTION FROM EEG SIGNAL

Lenka Lhotska, Vaclav Gerla, Jiri Bukartyk, Vladimir Krajca and Svojmil Petranek

Manual evaluation of long-term EEG recordings is very tedious, time consuming, and subjective process. The aims of automated processing are on one side to ease the work of medical doctors and on the other side to make the evaluation more objective. This paper addresses the problem of computer-assisted sleep staging. It describes ongoing research in this area. The proposed solution comprises several consecutive steps, namely EEG signal pre-processing, feature extraction, feature normalization, and application of decision trees for classification. The work is focused on the feature extraction step that is regarded as the most important one in the classification process.

DYNAMICAL PROPERTY OF PERIODIC OSCILLATIONS OBSERVED IN A COUPLED NEURAL OSCILLATOR NETWORK FOR IMAGE SEGMENTATION

Tetsuya Yoshinaga and Keníchi Fujimoto

We consider image segmentation using the LEGION (Locally-Excitatory Globally-Inhibitory Oscillator Network), and investigate dynamical properties of a modified LEGION, described by noise-free or deterministic continuous ordinary differential equations. We clarify a phenomenon of image segmentation corresponds to the appearance of a synchronized periodic solution, and the ability of segmentation depends on its symmetric properties. We study bifurcations of periodic solutions by using a computational method based on the qualitative dynamical system theory.

ARAFAC CLASSIFICATION OF LAMB CARCASS SOFT TISSUES IN COMPUTER TOMOGRAPHY (CT) IMAGE STACKS

Jørgen Kongsro

Computer Tomography is shown to be an efficient and cost-effective tool for classification and segmentation of soft tissues in animal carcasses. By using 15 fixed anatomical sites based on vertebra columns, 120 lamb carcasses were CT scanned in Norway during autumn of 2005. Frequency distributions of CT values (HU [-200,200]) of soft tissues from each image were obtained. This yielded a 3-way data set (120 samples * 400 CT values * 15 anatomical sites). The classification of the soft tissues was done by multi way Parallel Factor Analysis (PARAFAC), which resulted in 3 components or soft tissues classified from the images; fat, marbled and lean muscle tissue.

BIOPHYSICAL MODEL OF A MUSCLE FATIGUE PROCESS INVOLVING Ca2+ RELEASE DYNAMICS UPON THE HIGH FREQUENCY ELECTRICAL STIMULATION

Piotr Kaczmarek

The aim of this study is to create a model which enables to explain the muscle fibre contraction due to various stimulation programs. The model accounts for $Ca^{2+}$ release dynamics both as a result of an action potential and of a stimulus shape, duration and frequency. It has been assumed that the stimulus can directly activate the voltage-dependent receptors (dihydropiridine receptors) responsible for a $Ca^{2+}$ release. The stimulation programs consisted of standard stimulation trains made of low and middle frequency square pulses. High frequency modulating harmonic signals have been tested to investigate the fibre fatigue effect. It has been observed that fatigue effect factors depend on the selected stimulation program. The results reveal that the fatigue effect could be minimized by changing the shape and frequency of the stimulation waveform. Such the model could be useful for a preliminary selection and optimization of the stimulus shape and the stimulation trains, thus reducing the number of in vivo experiments.

AUTOMATIC DETECTION OF IN VITRO CAPILLARY TUBE NETWORK IN A MATRIGEL ANALYSIS

Eric Brassart, Cyril Drocourt, Jacques Rochette, Michel Slama and Carole Amant

Angiogenesis, the formation of new capillary blood vessels from pre-existing vessel, has become an important area of scientific research. Numerous in vivo and in vitro angiogenesis assays have been developed in order to test molecules designed to cure deregulated angiogenesis. But unlike most animal models, most in vitro angiogenesis models are not yet automatically analysed and conclusion and data quantification depend on the observer’s analysis. In our study, we will develop a new automatic in vitro matrigel angiogenesis analysis allowing tube length and the number of tubes per cell islets as well as cell islet and tubule mapping to be determined, percentage of vascularisation area, the determination of ratio of tubule length per number of cells in cell islet and, ratio length/width per tubule determination. This new method will also take image noise into account. Our method uses classical imaging quantification. For the first image processing we used image segmentation (Sobel type edge detection) and artefact erasing (morphologic operator). Subsequent image processing used Snakes: Active contour models in order to precisely detect cells or cell islets. We suggest that this new automated image analysis method for quantification of in vitro angiogenesis will give the researcher vascular specific quantified data that will help in the comparison of samples.

A SUPERVISED LEARNING APPROACH BASED ON THE CONTINUOUS WAVELET TRANSFORM FOR R SPIKE DETECTION IN ECG

G. de Lannoy, A. de Decker and M. Verleysen

One of the most important tasks in automatic annotation of the ECG is the detection of the R spike. The wavelet transform is a widely used tool for R spike detection. The time-frequency decomposition is indeed a powerful tool to analyze non-stationary signals. Still, current methods use consecutive wavelet scales in an a priori restricted range and may therefore lack adaptivity. This paper introduces a supervised learning algorithm which learns the optimal scales for each dataset using the annotations provided by physicians on a small training set. For each record, this method allows a specific set of non consecutive scales to be selected, based on the record characteristics. The selected scales are then used on the original long-term ECG signal recording and a hard thresholding rule is applied on the derivative of the wavelet coefficients to label the R spikes. This algorithm has been tested on the MIT-BIH arrhythmia database and obtains an average sensitivity rate of 99.7% and average positive predictivity rate of 99.7%.

ROBUST CENTROID-BASED CLUSTERING USING DERIVATIVES OF PEARSON CORRELATION

Marc Strickert, Nese Sreenivasulu, Thomas Villmann and Barbara Hammer

Modern high-throughput facilities provide the basis of -omics research by delivering extensive biomedical data sets. Mass spectra, multi-channel chromatograms, or cDNA arrays are such data sources of interest for which accurate analysis is desired. Centroid-based clustering provides helpful data abstraction by representing sets of similar data vectors by characteristic prototypes, placed in high-density regions of the data space. This way, specific modes can be detected, for example, in gene expression profiles or in lists containing protein and metabolite abundances. Despite their widespread use, k-means and self-organizing maps (SOM) often only produce suboptimum results in centroid computation: the final clusters are strongly dependent on the initialization and they do not quantize data as accurately as possible, particularly, if other than the Euclidean distance is chosen for data comparison. Neural gas (NG) is a mathematically rigorous clustering method that optimizes the centroid positions by minimizing their quantization errors. Originally formulated for Euclidean distance, in this work NG is mathematically generalized to give accurate and robust results for the Pearson correlation similarity measure. The benefits of the new NG for correlation (NG-C) are demonstrated for sets of gene expression data and mass spectra.

A PROBABILISTIC TRACKING APPROACH TO ROOT MEASUREMENT IN IMAGES - Particle Filter Tracking is used to Measure Roots, via a Probabilistic Graph

Andrew French, Malcolm Bennett, Caroline Howells, Dhaval Patel and Tony Pridmore

This paper introduces a new methodology to aid the tracing and measurement of lines in digital images. The techniques in this paper have specifically been applied to the labour intensive process of measuring roots in digital images. Current manual methods can be slow and error prone, and so we propose a semi-automatic way to trace the root image and measure the corresponding length in the image plane. This is achieved using a particle filter tracker, normally applied to object tracking though time, to trace along a root in an image. The samples the particle filter generates are used to build a probabilistic graph across the root location in the image, and this is traversed to produce a final estimate of length. The software is compared to real-world and artificial length data. Extensions of the algorithm are noted, including the automatic detection of the end of the root, and the detection of multiple growth modes using a mixed state particle filter.

FEASABILITY OF YEAST AND BACTERIA IDENTIFICATION USING UV-VIS-SWNIR DIFUSIVE REFLECTANCE SPECTROSCOPY

J. S. Silva, R. C. Martins, A. A. Vicente and J. A. Teixeira

UV-VIS spectroscopy is a powerfull qualitative and quantitative technique used in analytical chemistry, which gives information about electronic transitions of electrons in molecular orbitals. As in UV-VIS spectra there is no direct information on characteristic organic groups, vibrational spectroscopy (e.g. infrared) has been preferred for biological applications. In this research, we try to use state-of-the-art fiber optics probes to obtain UV-VIS-SWNIR diffusive reflectance measurements of yeasts and bacteria colonies on plate count agar in the region of 200-1200nm; in order to discriminate the following microorganisms: i) yeasts: Saccharomyces cerevisiae, Saccharomyces bayanus, Candida albicans, Yarrowia lipolytica; and ii) bacteria: Micrococcus luteus, Pseudomonas fluorescens, Escherichia coli, Bacillus cereus. Spectroscopy results show that UV-VIS-SWNIR has great potential for identifying microorganisms on plate count agar. Scattering artifacts of both colonies and plate count agar can be significantly removed using a robust mean scattering algorithm, allowing also better discriminations between the scores obtained by singular value decomposition. Hierarchical clustering analysis of UV-VIS and VIS-SWNIR decomposed spectral scores lead to the conclusion that the use of VIS-SWNIR light source produces higher discrimination ratios for all the studied microorganisms, presenting great potential for developing biotechnology applications.

ENHANCED ANALYSIS OF UTERINE ACTIVTY USING SURFACE ELECTROMYOGRAPHY

A. Herzog, L. Reicke, M. Kröger, C. Sohn and H. Maul

This contribution presents a new approach for the enhanced analysis of uterine surface electromyography (EMG). First, a pulse detection separates the pulses, which contain the essential information about the uterine contractibility, from the flat line sections during relaxation. The functionality of this semi-automatic algorithm is controlled by two comprehensible parameters. Subsequently, the mean frequency, which serves as a criterion for imminent delivery, is evaluated from the extracted pulses. Although the pulse detection reduces the deviation of the mean frequency significantly, the results are still sensitive to parameter variations in the pulse detection. A stochastic analysis based on the Karhunen-Loève transform (KLT) derives generalised patterns, the eigenforms, from the pulse ensemble. The mean frequency of the first eigenform is less sensitive to parameter variations. Additionally, the correlation between the eigenforms of the left and right surface electrode can serve as a criterion for the measurement's quality.

BIOMIMETIC FLOW IMAGING WITH AN ARTIFICIAL FISH LATERAL LINE

Nam Nguyen, Douglas Jones, Saunvit Pandya, Yingchen Yang, Nannan Chen, Craig Tucker and Chang Liu

Recent studies have discovered that almost all fish possess a flow-sensing system along their body, called the lateral line, that allows them to perform various behaviours such as schooling, preying, and obstacle or predator avoidance. Inspired from this, our group has built artificial lateral lines from newly-developed flow sensors using Micro-Electro-Mechanical Systems (MEMS) technology. To make our lateral line a functional sensory system, we develop an adaptive beamforming algorithm (applying Capon’s method) that provides our lateral line with the capability of imaging the locations of oscillating dipoles in a 3D underwater environment. To help our sensor arrays adapt to the environment for better performance, we introduce a self-calibration algorithm that significantly improves the image accuracy. Finally, we derive the Cramer-Rao Lower Bound (CRLB) that represents the fundamental perfomance limit of our system and provides guidance in optimizing artificial lateral-line systems.

MULTIPLE SCALE NEURAL ARCHITECTURE FOR RECOGNISING COLOURED AND TEXTURED SCENES

Francisco Javier Díaz-Pernas, Míriam Antón-Rodríguez, Víctor Iván Serna-González José Fernando Díez-Higuera and Mario Martínez-Zarzuela

A dynamic multiple scale neural model for recognise colour images of textured scenes is proposed. This model combines colour and textural information to recognise coloured textures through the operation of two main components: segmentation component formed by the Colour Opponent System (COS) and the Chromatic Segmentation System (CSS); and recognition component formed by pattern generation stages and Fuzzy ARTMAP neural network. Firstly, the COS module transforms the RGB chromatic input signals into a bio-inspired codification system (L, M, S and luminance signals), and then it generates the opponent channels (black-white, L-M and S-(L+M)). The CSS module incorporates contour extraction, double opponency mechanisms and diffusion processes in order to generate coherent enhancing regions in colour image segmentation. These colour region enhancements along with the local textural features of the scene constitute the recognition pattern to be sent into the Fuzzy ARTMAP network. The structure of the CSS architecture is based on BCS/FCS systems, thus, maintaining their essential qualities such as illusory contours extraction, perceptual grouping and discounting the illuminant. But base models have been extended to allow colour stimuli processing in order to obtain general purpose architecture for image segmentation with later applications on computer vision and object recognition. Some comparative testing with other models is included here in order to prove the recognition capabilities of this neural architecture.

AUTOMATIC COUINAUD LIVER AND VEINS SEGMENTATION FROM CT IMAGES

Dário A. B. Oliveira, Raul Q. Feitosa and Mauro M. Correia

This paper presents an algorithm to segment the liver structures on computed tomography (CT) images according to the Couinaud orientation. Our method firstly separates the liver from the rest of the image. Then it segments the vessels inside the liver area using a region growing technique combined with hysteresis thresholding. It separates the vessels in segments without any bifurcation, and using heuristics based on anatomy, it classifies all vessel segments as hepatic or portal vein. Finally, the method estimates the planes that best fit each of the three branches of the segmented hepatic veins and the plane that best fits the portal vein. These planes define the subdivision of the liver in the Couinaud segments. An experimental evaluation based on real CT images demonstrated that the outcome of the proposed method is generally consistent with a visual segmentation.

MULTI-CHANNEL BIOSIGNAL ANALYSIS FOR AUTOMATIC EMOTION RECOGNITION

Jonghwa Kim and Elisabeth André

This paper investigates the potential of physiological signals as a reliable channel for automatic recognition of user's emotial state. For the emotion recognition, little attention has been paid so far to physiological signals compared to audio-visual emotion channels such as facial expression or speech. All essential stages of automatic recognition system using biosignals are discussed, from recording physiological dataset up to feature-based multiclass classification. Four-channel biosensors are used to measure electromyogram, electrocardiogram, skin conductivity and respiration changes. A wide range of physiological features from various analysis domains, including time/frequency, entropy, geometric analysis, subband spectra, multiscale entropy, etc., is proposed in order to search the best emotion-relevant features and to correlate them with emotional states. The best features extracted are specified in detail and their effectiveness is proven by emotion recognition results.

BIOSIGNALS ANALYSIS AND ITS APPLICATION IN A PERFORMANCE SETTING - Towards the Development of an Emotional-Imaging Generator

Mitchel Benovoy, Jeremy R. Cooperstock and Jordan Deitcher

The study of automatic emotional awareness of human subjects by computerized systems is a promising avenue of research in human-computer interaction with profound implications in media arts and theatrical performance. A novel emotion elicitation paradigm focused on self-generated stimuli is applied here for a heightened degree of confidence in collected physiological data. This is coupled with biosignal acquisition (electrocardiogram, blood volume pulse, galvanic skin response, respiration, phalange temperature) for determination of emotional state using signal processing and pattern recognition techniques involving sequential feature selection, Fisher dimensionality reduction and linear discriminant analysis. Discrete emotions significant to Russell’s arousal/valence circumplex are classified with an average recognition rate of 90%.

BIO-INSPIRED IMAGE PROCESSING FOR VISION AIDS

C. Morillas, F. Pelayo, J. P. Cobos, A. Prieto and S. Romero

We present in this paper a system conceived to perform a bioinspired image processing and different output encoding schemes, oriented to the development of visual aids for the blind or for visually-impaired patients. We remark some of its main features, as the possibility of combining different image processing modalities (colour, motion, depth, etc.) and different output devices (Head Mounted Displays, headphones, and microelectrode arrays), as well as its implementation on a reconfigurable chip (FPGA) or a specific VLSI chip, which allows working in real time on a portable equipment. A software design environment has been developed for the simulation and the automatic synthesis of the processing models into a hardware platform.

AN EVALUATION OF THE RELAXATION EFFECT OF MUSIC BASED ON THE RELATIONSHIPS BETWEEN THE CONDITION OF PULSE AND MUSIC TEMPO USING THE EEG AND HRV BASED INDICATORS

Genki Murayama, Shohei Kato, Hidenori Itoh and Tsutomu Kunitachi

This paper attempt to investigate the relationships between relaxation effect of music and rhythm of human body (in this paper fingerplethysmogram (so called "pulse") is adopted) using EEG and HRV based two relaxation indicators. We focus on following viewpoints: synchronization between pulse and music, the tendency of pulse beat and pulse-music tempo ratio. This paper reports the experimental results that the pulse decreasing state is effective for EEG based indicator while HRV based indicator is high value at the pulse increasing state. Furthermore, we classify subjects into 3 groups by the analysis of synchronization between pulse and music tempo. This papar also reports the analysis of relationships between pulse-music tempo ratio and relaxation effect under the classification.

ILLUMINATION NORMALIZATION FOR FACE RECOGNITION - A Comparative Study of Conventional vs. Perception-inspired Algorithms

Peter Dunker and Melanie Keller

Face recognition has been actively investigated by the scientific community and has already taken its place in modern consumer software. However, there are still major challenges remaining e.g. preventing negative influence from varying illumination, even with well known face recognition systems. To reduce the performance drop off caused by illumination, normalization methods can be applied as pre-processing step. Well known approaches are histogram modifications, linear regression or local operations. In this publication we present the results of a two-step evaluation for real-world applications of a wide range of state-of-the-art illumination normalization algorithms. Further we present a new taxonomy of these methods and depict advanced algorithms motivated by the pre-eminent human abilities of illumination normalization. Additionally we introduce a recent bio-inspired algorithm based on diffusion filters that outperforms most of the known algorithms. Finally we deduce the theoretical potentials and practical applicability of illumination normalization methods from the evaluation results.

FINDING APPROXIMATE LANGUAGE PATTERNS

Samuel W. K. Chan

This paper proposes a model of semantic labeling based on the edit distance. The dynamic programming approach stresses on a non-exact string matching technique that takes full advantage of the underlying grammatical structure of 65,000 parse trees in a Treebank. The approach is based on the assumption that human language understanding is relevant to concrete past language experiences rather than any abstract linguistic rules. This shallow technique is inspired by the research in the area of bio-molecular sequences analysis which advocates high sequence similarity usually implies significant function or structural similarity. The model described has been implemented. Experimental results for recognizing various labels in 10,000 sentences are used to justify its significances.

INVESTIGATION OF ENTROPY AND COMPLEXITY MEASURES FOR DETECTION OF SEIZURES IN THE NEONATE

Ehsan Chah, Barry R. Greene, Geraldine B. Boylan and Richard B. Reilly

The performance of three Entropy measures and a complexity measure in detecting EEG seizures in the neonate were investigated in this study. A dataset containing EEG recordings from 11 neonates, with documented electrographic seizures, were employed as the basis for the study. Based on patient independent tests Shannon Entropy was the best in discriminating seizures and non-seizures EEG in the neonate. Lempel-Ziv complexity and Multi-scale Entropy were second and third respectively, while Sample Entropy did not prove a useful feature for discriminating seizure patterns from non-seizure patterns.

MEASURING CHANGES OF 3D STRUCTURES IN HIGH-RESOLUTION μCT IMAGES OF TRABECULAR BONE

Norbert Marwan, Jürgen Kurths, Peter Saparin and Jesper S. Thomsen

The appearances of pathological changes of bone can be various. Determination of apparent bone density is commonly used for diagnosing bone pathological conditions. However, in the last years the structural changes of trabecular bone have received more attention as bone densitometry alone cannot explain all variation in bone strength. The rapid progress in high resolution 3D $\mu$CT imaging facilitates the development of new 3D measures of complexity for assessing the spatial architecture of trabecular bone. We have developed a novel approach which is based on 3D complexity measures in order to quantify spatial geometrical properties. These measures evaluate different aspects of organization and complexity of trabecular bone spatial architecture, such as complexity of its surface, node complexity, or trabecular bone surface curvature. In order to quantify the differences in the trabecular bone architecture at different stages of bone involution, the developed complexity measures were applied to 3D data sets acquired by $\mu$CT from human proximal tibiae and lumbar vertebrae. The results obtained by the complexity measures were compared with results provided by static histomorphometry. We have found clear relationships between the proposed measures and different aspects of bone architecture assessed by the histomorphometry (e.g.~complexity of bone surface).

ENDOCARDIAL SEGMENTATION IN CONTRAST ECHOCARDIOGRAPHY VIDEO WITH DENSITY BASED SPATIO-TEMPORAL CLUSTERING

Prashant Bansod, U. B. Desai and Nitin Burkule

We present a spatio-temporal clustering algorithm for detection of endocardial contours in short axis (SAX) contrast echocardiographic image sequences. A semiautomatic method for segmentation of left ventricle in SAX videos is proposed which uses this algorithm and requires minimal expert intervention. Expert is required to specify candidate points of the contour only in the first frame of the sequence. The initial contour is approximated by fitting an ellipse in the region defined by the points specified. This region is identified as the principal cluster corresponding to the left ventriclular cavity. Later the density based clustering was applied for regularization on the inital contour. We have extended the DBSCAN algorithm for identification of the principal cluster corresponding to the left ventricle from the image. The algorithm also incorporates the temporal information from the adjacent frames during the segmentation process. The algorithm developed was applied to $10$ data sets over full cardiac cycle and the results were validated by comparing computer generated boundaries to those manually outlined by one expert. The maximum error in the contours detected was +/- 2.9 mm. The spatio-temporal clustering algorithm proposed in this paper offers an efficient semiautomatic segmentation of heart chambers in 2D contrast echocardiography sequences.

AN EVOLUTIONARY APPROACH TO MULTIVARIATE FEATURE SELECTION FOR FMRI PATTERN ANALYSIS

Malin Aberg, Line Löken and Johan Wessberg

Multivariate pattern recognition has recently gained in popularity as an alternative to univariate fMRI analyis, although the exceedingly high spatial dimensionality has proven problematic. Addressing this issue, we have explored the effectiveness of evolutionary algorithms in determining a limited number of voxels that, in combination, optimally discriminate between single volumes of fMRI. Using a simple multiple linear regression classifier in conjunction with as few as five evolutionarily selected voxels, a subject mean single trial binary prediction rate of 74.3% was achieved on data generated by tactile stimulation of the arm compared to rest. On the same data, feature selection based on statistical parametric mapping resulted in 63.8% correct classification. Our evolutionary feature selection approach thus illustrates how, using appropriate multivariate feature selection, surprising amounts of information can be extracted from very few voxels in single volumes of fMRI. Moreover, the resulting voxel discrimination relevance maps (VDRMs) showed considerable overlap with traditional statistical activation maps, providing a model-free alternative to statistical voxel activation detection.

BI-LEVEL IMAGE THRESHOLDING - A Fast Method

António dos Anjos and Hamid Reza Shahbazkia

Images with two dominant intensity levels are easily manually thresholded. For automatic image thresholding, most of the effective techniques are either too complex or too eager of computer resources. This paper presents an iterative method for image thresholding that is simple, fast, effective and that requires minimal computer processing power. Images of micro and macroarray of genes have characteristics that allow the use of the presented method for thresholding.

FUZZY MRF MODELS WITH MULTIFRACTAL ANALYSIS FOR MRI BRAIN TISSUE CLASSIFICATION

Liang Geng and Weibei Dou

This paper introduces multifractal analysis to the Fuzzy Markov Random Field (MRF) Model, used for brain tissue classification of Magnetic Resonance Images (MRI). The traditional classifying method using Fuzzy MRF Model is already able to calculate out the memberships of each voxel, to solve the Partial Volume Effect (PVE). But its accuracy is relatively low, for its spatial resolution is not high enough. Therefore the multifractal analysis is brought in to raise the accuracy by providing local information. The improved method is tests on both simulated data and real images, where results on membership average errors and position errors are calculated. These results show that the improved method can provide much higher accuracy.

CARDIAC MAGNETIC FIELD MAP TOPOLOGY QUANTIFIED BY KULLBACK-LEIBLER ENTROPY IDENTIFIES PATIENTS WITH HYPERTROPHIC CARDIOMYOPATHY

A. Schirdewan, A. Gapelyuk, R. Fischer, L. Koch, H. Schütt, U. Zacharzowsky, R. Dietz, L. Thierfelder and N. Wessel

Hypertrophic Cardiomyopathy (HCM) is defined clinically by the growing/thickening of especially the left heart muscle. In up to 70 % of cases, there is a family history of this condition. The individual risk for affected patients strongly varies and depends on the individual manifestation of the disease. Therefore, an early detection of the disease and identification of high-risk subforms is desirable. In this study we investigated the capability of cardiac magnetic field mapping (CMFM) to detect patients suffering from HCM (n=33, 43.8 ± 13 years, 13 women, 20 men; vs. a control group of healthy subjects, n=57, 39.6 ± 8.9 years; 22 women, 35 men; vs. patients with confirmed cardiac hypertrophy due to arterial hypertension, n=42, 49.7 ± 7.9 years, 15 women, 27 men). We introduce for the first time a combined diagnostic approach based on map topology quantification using Kullback-Leibler (KL) entropy and regional magnetic field strength parameters. The cardiac magnetic field was recorded over the anterior chest wall using a multichannel-LT-SQUID system. We show that our diagnostic approach allows not only detecting HCM affected individuals, but also discriminates different forms of the disease. Thus, CMFM including KL entropy based topology quantifications is a suitable tool for HCM screening.

PHONETOGRAPHY DATABASE

Lídia Cristina da Silva Teles, Maria Inês Pegoraro-Krook and Marcos Kenned Magalhães

The aim of this work was to create a software that, from the phonetography measures of eldery women, generates the phonetogram, evaluates its area, vocal extension (VE), and the dynamic extension (DE) and elaborates a database. The phonetography exams were carried out based on the European Phoniatrics Rules. The software tools used for development were Delphi® and Paradox®. The results related to the voice evaluation of eldery women compares favorably with the normal aging process. The software stores and recovers the exams data as well as evaluates voice characteristics and presents graphical outputs in an appropriate way.

DELAYED RECOVERY OF CARDIOVASCULAR AUTONOMIC FUNCTION AFTER MITRAL VALVE SURGERY - Evidence for Direct Trauma?

R. Bauernschmitt, B. Retzlaff, N. Wessel, H. Malberg, G. Brockmann, C. Uhl and R. Lange

Baroreflex Sensivity (BRS) and heart rate variability (HRV) have significant influence on the patients’ prognosis after cardiovascular events. The following study was performed to assess the differences in the postoperative recovery of the autonomic regulation after mitral valve (MV) surgery and aortic valve (AV) surgery with heart-lung machine. 43 consecutive male patients were enrolled in a prospective study; 26 underwent isolated aortic valve surgery and 17 isolated mitral valve surgery. Blood pressure, ECG and respiratory rate were recorded the day before, 24h after surgery and one week after surgery. BRS was calculated according to the Dual Sequence Method, time and frequency parameters of HRV were calculated using standard methods. There were no major differences between the two groups in the preoperative values. At 24 h a comparable depression of HRV and BRS in both groups was observed, while at 7 days there was partial recovery in AV-patients, which was absent in MV-patients: p (AV vs. MV)<0,001. While the response of the autonomic system to surgery is similar in AV- and MV-patients, there obviously is a decreased ability to recover in MV-patients, probably attributing to traumatic lesions of the autonomic nervous system by opening the atria. Ongoing research is required for further clarification of the pathophysiology of this phenomenon and to establish strategies to restore autonomic function.

ANALYSIS ALGORITHMS FOR A FIRST-AID SENSOR - Detecting Vitality Parameters such as Pulse and Respiration

Daniel Wettach, Marc Jaeger, Armin Bolz and Timur Oezkan

In this paper the software algorithms necessary to analyze the signal provided by a first-aid sensor system that detects pulse and respiration at a single point are described. In an opinion poll four of five inexperienced first responders were interested in using this kind of system as support in emergency situations. Especially the intelligent detection of respiration is hardly popular today and in most cases only possible offline. The software also controls several visual indicators that assist the first aider in quickly determining the state of the patient.

COMPARATIVE STUDY OF BLIND SOURCE SEPARATION METHODS FOR RAMAN SPECTRA - Application on Numerical Dewaxing of Cutaneous Biopsies

Valeriu Vrabie, Cyril Gobinet, Michel Herbin and Michel Manfait

Raman spectroscopy is a powerful tool for the study of molecular composition of biological samples. Digital processing techniques are needed to separate the wealthy but complex information recorded by Raman spectra. Blind source separation methods can be used to efficiently extract the spectra of chemical constituents. We propose in this study to analyze the performances of four blind source separation methods. Two Independent Component Analysis methods using the JADE and FastICA algorithms are based uniquely on the independence of the spectra. The Non-Negative Matrix Factorization takes into account only the positivity of underlying spectra and mixing coefficients. The Maximum Likelihood Positive Source Separation assumes both the independence and positivity of the spectra. A realistic simulated dataset allows a quantitative study of these methods while real a dataset recorded on a paraffin-embedded skin biopsy provides a qualitative study.

WAVELET-BASED REAL-TIME ECG PROCESSING FOR A WEARABLE MONITORING SYSTEM

S. Zaunseder, W.-J. Fischer, R. Poll and M. Rabenau

This paper presents the implementation of an ambulatory ECG monitoring system. Following thereby we focus on the wavelet-based signal processing. The monitoring system comprises current trends of ambulatory ECG monitoring like integration of hardware in clothing, the use of low power components, wireless transmission of data by Bluetooth and the use of a PDA. Differing from other approaches, the signal processing was located close to the sensor, thus allowing more variability in further data handling. From limited resources (an ultra-low power µC was used) and high demands on the signal processing arises the need for a signal processing method which meets the special demands of the ambulatory application. Based on numerous studies concerning the wavelet transform and its implementation delivered by literature, we realized a wavelet based method especially adapted to the real-time requirements. To date, all tests proved a low computational load while the reliability of the analysis was preserved, thus pointing out the possibilities of the real-time signal processing under use of an ultra-low power µC.

SINGLE PARTICLE DETECTION - A Diagnostic Tool for Particle Associated Diseases like Alzheimer’s Disease and Creutzfeldt-Jakob Disease

Eva Birkmann, Susanne Aileen Funke, Detlev Riesner and Dieter Willbold

Neurodegenerative diseases like Alzheimer’s disease (AD), prion diseases and others are progressive and lethal. High-molecular weight aggregates of the Amyloid-β-peptides (Aβ) or of the misfolded prion protein (PrP) are found in patients afflicted by AD or prion diseases, respectively. Despite of many attempts, neither a therapy for recovery, nor an early diagnosis at preclinical stages is available. Psychological tests and imaging approaches not directly related with a secure disease marker are in use only for late stages of the disease. The Creutzfeldt-Jakob-disease (CJD), a human prion disease, is caused by accumulation of aggregates consisting of an abnormally shaped version of PrP. CJD is diagnosed with certainty only by neuropathology post mortem. In this study a multidisciplinary development of a novel mode of single particle counting of immobilized Aβ and PrP aggregates as the most direct biomarkers for Alzheimer’s disease and Prion diseases, respectively, is introduced. For ultrasensitive detection of aggregates, the suitable instrumentation as well as data acquisition and data analysis are developed using single molecule detection and advanced laser scanning fluorescence techniques. In the novel assay development effort biochemistry, detection and analysis were improved to detect single aggregates immobilised on a surface. First results show the improvement of single particle detection of PrP-aggregates of TSE-afflicted cattle and hamsters as well as synthetic Aβ-aggregates.

EEG HEADSET FOR NEUROFEEDBACK THERAPY - Enabling Easy Use in the Home Environment

Joran van Aart, Eelco R. G. Klaver, Christoph Bartneck, Loe M. G. Feijs and Peter J. F. Peters

In this paper we discuss our vision on future neurofeedback therapy and present an EEG headset designed to realize that vision. We analyse problems of the current situation and debate for a change in focus towards a situation in which neurofeedback therapy will ultimately be as easy as taking an aspirin. Furthermore we argue for a gaming approach as training, to increase enjoyment in neurofeedback therapy using motivation. We describe the headset that has been developed to achieve enjoyable neurofeedback therapy in the home environment and conclude with an evaluation of this headset.

A FULLY AUTOMATIC RED-EYES DETECTION AND CORRECTION ALGORITHM BASED ON UNIFORM COLOR METRIC AND BINOCULAR GEOMETRIC CONSTRAINT

Chun-Hsien Chou, Kuo-Cheng Liu and Shao-Wei Su

Red-eye is a highly objectionable defect that often occurs in digital images taken with a flash by modern small cameras. Although many red-eye reduction algorithms were proposed and equipped in most of the digital cameras, none of these algorithms is effective enough. In this paper, an algorithm for automatic de-tection and correction of red-eyes is proposed. The color detector based on uniform color metric is devel-oped to locate regions of major colors including red-eye color and skin tone. The structure of major colors is adopted to locate candidate red-eye regions. The geometric relationship between the dimension of the human pupil and binocular distance is employed to eliminate most false positives (image regions that look like red-eyes but are not). More than one pairs of red-eyes snapped in different view angles are detected by the proposed algorithm. Detected red-eyes are then corrected by modifying chroma, hue angles and lumi-nance of the associated pixels such that red color is removed while maintaining a natural look of the eye. Simulation results show that the proposed algorithm is pretty robust and effective.

VOICE SIGNALS CHARACTERIZATION THROUGH ENTROPY MEASURES

Paulo Rogério Scalassara, María Eugenia Dajer, Carlos Dias Maciel and José Carlos Pereira

Human voice has been a matter of interest for different areas as technological development and medical sciences. In order to understand the dynamic complexity of healthy and pathologic voice, researchers have developed tools and methods for analysis. Recently nonlinear dynamics has shown the possibility to explore the dynamic nature of voice signals from a different point of view. The purpose of this paper is to apply entropy measures and phase space reconstruction technique to characterize healthy and nodule affected voices. Two groups of samples were used, one from healthy individuals and the other from people with nodule in the vocal fold. They are recordings of sustained vowel /a/ from Brazilian Portuguese. The paper shows that nonlinear dynamical methods seem to be a suitable technique for voice signal analysis, due to the chaotic component of the human voice. Since the nodule pathology is characterized by an increase in the signal's complexity and unpredictability, measures of entropy are well suited due to its sensibility to uncertainty. The results showed that the nodule group had a higher entropy values. This suggests that these techniques may improve and complement the recent voice analysis methods available for clinicians.

SPEAKER RECOGNITION USING DECISION FUSION

M. Chenafa, D. Istrate, V. Vrabie and M. Herbin

Biometrics systems have gained in popularity for the automatic identification of living persons. The use of the voice as biometric characteristic offers advantages such as: is well accepted, it works with a regular microphone and the hardware costs are reduced. However, the performance of a voicebased biometric system easily degrades in the presence of a mismatch between training and testing conditions due to different factors. This paper presents a new speaker recognition system based on decision fusion. The fusion is based on two identification systems: a speaker identification system (text-independent) and a keywords identification system (speaker-independent). These systems calculate likelihood ratios between the model of a test signal and different models of the database. The fusion uses these results to identify the couple speaker/password corresponding to the test signal. A verification system is then applied on a second test signal in order to confirm or infirm the identification. The fusion step improves the false rejection rate (FRR) from 21,43% to 7,14% but increase also the false acceptation rate (FAR) from 21,43% to 28,57%. The verification step makes however a significant improvement on the FAR (from 28,57% to 14,28%) while it keeps constant the FRR.

A HYBRID SEGMENTATION FRAMEWORK USING LEVEL SET METHOD FOR CONFOCAL MICROSCOPY IMAGES

Quan Xue, Severine Degrelle, JuhuiWang, Isabelle Hue and Michel Guillomot

Based on variational level set approaches, we present a hybrid framework with quality control for segmentation of cellular nuclei in confocal microscopy images. The nuclei are firstly modelled into blobs with some additive noise, and then Laplacian of Gaussian filter is applied as a blob-detector. Secondly, we reformulate the segmentation as a front propagation. The energy minimization of fast marching is obtained towards the boundaries of the desired objects. We select multi-points instead of one as candidates, and let them travel in their local areas to select the best seeds as the initial conditions. Then the gravity center of each nuclear can be computed. In order to achieve the very high accuracy rates required in biological research, our framework is designed in a scalable-structure so that a selectable module will provide an interface to manually check the errors by analyzer. From the appropriate centers of nuclei, the original image will be divided into a Voronoi mesh. In each local region geodesic active contour evolves toward the minimum of the designed energy, and the influence of internal and external forces will fit the accurate nuclei edges. Post-processing is a supplementary stage for potential errors. Our algorithm is tested on the confocal microscopy images from bovine trophoblast. The experimental results show that cell nuclei can be effectively segmented with a controllable accuracy and topological changes in clusters can be naturally managed. Assuming the interactivity, a success rate of 100% can be achieved.

A BIO-INSPIRED CONTRAST ADAPTATION MODEL AND ITS APPLICATION FOR AUTOMATIC LANE MARKS DETECTION

Valiantsin Hardzeyeu and Frank Klefenz

Even in significant light intensity fluctuations human beings still can sharply perceive the surrounding world under various light conditions: from starlight to sunlight. This process starts in the retina, a tiny tissue of a quarter of a millimeter thick. Based on retinal processing principles, a bio-inspired computational model for online contrast adaptation is presented. The proposed method is developed with the help of the fuzzy theory and corresponds to the models of the retinal layers, their interconnections and intercommunications, which have been described by neurobiologists. The retinal model has been coupled in the successive stage with the Hough transformation in order to create a robust lane marks detection system. The performance of the system has been evaluated with the number of test sets and showed good results. In the conclusion the problems of further development and improvement of the existing model are discussed.

A MULTIMODAL PLATFORM FOR DATABASE RECORDING AND ELDERLY PEOPLE MONITORING

Hamid Medjahed, Dan Istrate, Jerome Boudy, Jean-Louis Baldinger, Bernadette Dorizzi, Imad Belfeki, Vinicius Martins, François Steenkeste and Rodrigo Andreao

This paper describes a new platform for monitoring elderly people living alone. An architecture is proposed, it includes three subsystems, with various types of sensors for different sensing modalities incorporated into a smart house. The originality of this system is the combination and the synchronization of three different televigilance modalities for acquiring and recording data. The paper focuses on the acquisition step of the system, usage and point out possibilities for future work.

BIOSIG - Standardization and Quality Control in Biomedical Signal Processing using the BioSig Project.

A. Schlögl, C. Vidaurre, Ernst Hofer, Thomas Wiener, Clemens Brunner, Reinhold Scherer and Franco Chiarugi

Biomedical signal processing is an important but underestimated area of medical informatics. In order to overcome this limitation, the open source software library BioSig has been established. BioSig provides reference implementations for biomedical signal processing questions. The tools can be used to compare the recordings of different equipment provider, it provides validated methods for artifact processing and supports over 40 different data formats (more than any other software in this area).

A NEW FRAMEWORK FOR REAL-TIME ADAPTIVE FUZZY MONITORING AND CONTROL FOR HUMANS UNDER PSYCHOPHYSIOLOGICAL STRESS

A. Nassef, C. H. Ting, M. Mahfouf, D. A. Linkens, P. Nickel, G. R. J. Hockey and A. C. Roberts

The first part of this paper assesses the operator functional state (OFS) of human operators based on a collection of psychophysiological and performance measures. Two types of adaptive fuzzy models, namely ANFIS (adaptive-network-based fuzzy inference system) and GA (genetic algorithm) based Mamdani fuzzy model, are employed to estimate the OFSs under a set of simulated process control tasks involved in an automation-enhanced Cabin Air Management System (aCAMS). The adaptive fuzzy modelling procedures are described and then validated using real-life data measured from such a simulated human-machine process control system. In the second part of this paper a real-time adaptive automation control system is proposed with the previously developed fuzzy modelling mechanisms representing the kernel of the system.

FAST AND ROBUST MID-SAGITTAL PLANE LOCATION IN 3D MR IMAGES OF THE BRAIN

Felipe P. G. Bergo, Guilherme C. S. Ruppert, Luiz F. Pinto and Alexandre X. Falcão

Extraction of the mid-sagittal plane (MSP) is an important step for brain image registration and asymmetry analysis. We present a fast MSP extraction method for 3D MR images, which is based on automatic segmentation of the brain and on heuristic maximization of cerebro-spinal fluid within the MSP. The method is shown to be robust to severe anatomical asymmetries between the hemispheres, caused by surgical procedures and lesions. The experiments used 64 MR images (36 pathological, 20 healthy, 8 synthetic) and the method found an acceptable approximation of the MSP in all images with a mean time of 60.0 seconds per image.

FALL DETECTOR BASED ON NEURAL NETWORKS

Rubén Blasco, Roberto Casas, Álvaro Marco, Victorián Coarasa, Yolanda Garrido and Jorge L. Falcó

Falls are one of the biggest concerns of elderly people. This paper addresses a fall detection system that uses an accelerometer to acquire body accelerations, ZigBee to send relevant data when a fall might have happened and a neural network to recognize fall patterns. The method used offers evident improved performance compared to traditional basic-threshold systems. Main advantage is that fall detection ratio is higher on neural network based systems. Another important issue is the high immunity to events not being falls, but with similar patterns (e.g. sitting in a sofa abruptly), usually confused with real falls. Minimization of these occurrences has big effect on the confidence the user have on the system.

CREST LINE AND CORRELATION FILTER BASED LOCATION OF THE MACULA IN DIGITAL RETINAL IMAGES

Castor Mariño, Manuel Gonzalez Penedo, Francisco Gonzalez and Simon Pena

The fovea is a spot located in the center of the macula, and responsible for sharp central vision. In this paper a method to detect the macula location and size is presented, as a first step towards the fovea location.In the first stage of the process, the retinal vessel tree is extracted through a crest line detector. Then, the main vessel arc is fitted to a parabolic curve using a polynomial fitting process, which will allow for the computation of the area where the optic disc is located. The last stage consists in the segmentation of the optic disc, by means of the combination of morphological operations and a deformable model. Then, following the morphological properties of the eye, the macula location and size is determined by means of a new correlation filter. Search with this filter is performed in a reduced area of interest, whose size and position is determined by means, again, of the morphological properties of the eye. The algorithm has proven to be fast and accurate in the set of test images, composed by 135 digital retinal images.

ANALYSIS OF HEART RATE AND BLOOD PRESSURE VARIABILITY IN PREGNANCY - New Method for the Prediction of Preeclampsia

H. Malberg, R. Bauernschmitt, T. Walther, A. Voss, Renaldo Faber, Holger Stepan and N. Wessel

Pre-eclampsia (PE) is a serious disorder with high morbidity and mortality occurring during pregnancy; 3%–5% of all pregnant women are affected. Although most pre-eclamptic patients show pathological uterine perfusion in the second trimester, this parameter has a positive predictive accuracy of only 30%, which makes it unsuitable for early, reliable prediction. The study is based on the hypothesis that alterations in cardiovascular regulatory behavior can be used to predict PE. Ninety-six pregnant women in whom Doppler investigation detected perfusion disorders of the uterine arteries were included in the study. Twentyfour of these pregnant women developed PE after the 30th week of gestation. During pregnancy, additional several noninvasive continuous blood pressure recordings were made over 30 min under resting conditions by means of a finger cuff. In the period between the 18th and 26th weeks of pregnancy, three special variability and baroreflex parameters were able to predict PE several weeks before clinical manifestation. Discriminant function analysis of these parameters was able to predict PE with a sensitivity and specificity of 87.5% and a positive predictive value of 70%. The combined clinical assessment of uterine perfusion and cardiovascular variability demonstrates the best current prediction several weeks before clinical manifestation of PE.

EXPERIMENTS ON SOLVING MULTICLASS RECOGNITION TASKS IN THE BIOLOGICAL AND MEDICAL DOMAINS

Paolo Soda

Multiclass learning problems can be cast as the task of assigning instances to a finite set of classes. Although in the wide variety of learning tools there exist some algorithms capable of handling polychotomies, many of the tools were designed by nature for dichotomies. In the literature, many techniques that decompose a polychotomy into a series of dichotomies have been proposed. One of the possible approaches, known as one-per-class, is based on a pool of binary modules, where each one distinguishes the elements of one class from those of the others. In this framework, we propose a novel reconstruction criterion, i.e. a rule that sets the final decision on the basis of the single binary classifications. It looks at the quality of the current input and, more specifically, it is a function of the reliability of each classification act provided by the binary modules. The approach has been tested on four biological and medical datasets and the achieved performance has been compared with the ones previously reported in the literature, showing that the method improves the accuracies so far.

IMPROVING AN AUTOMATIC ARRHYTHMIAS RECOGNISER BASED IN ECG SIGNALS

Jorge Corsino, Carlos M. Travieso, Jesús B. Alonso and Miguel A. Ferrer

In the present work, we have developed and improved a tool for the automatic arrhythmias detection, based on neural network with the “more-voted” algorithm. Arrhythmia Database MIT has been used in the work in order to detect eight different states, seven are pathologies and one is normal. The unions of different blocks and its optimization have found an improvement of success rates. In particular, we have used wavelet transform in order to characterize the patron wave of electrocardiogram (ECG), and principal components analysis in order to improve the discrimination of the coefficients. Finally, a neural network with more-voted method has been applied.

SOBI WITH ROBUST ORTHOGONALIZATION TO REMOVE THE ARTEFACT STIMULUS IN EVOKED POTENTIAL - 5Hz Current Sinusoidal Stimulus

Eduardo de Queiroz Braga, Carlos Julio Tierra-Criollo and Gilberto Mastrocola Manzano

The psychophysical evaluation of the sensibility of the thin and thick fibers with sinusoidal current stimulation was proposed in the 80s. After that, researches observed that 5 Hz stimulus would be related to the thin unmyelinated fiber. This work aims a quantitative analysis of the cerebral cortex response to 5 Hz stimulus, through the identification of the latency components of the evoked potential (EP) that were estimated by the coherent mean after remove the artefact stimulus by using the Independent Component Analysis. Electroencephalography (EEG) signals were collected at Cz electrode (10-20 International Standard System) of 5 volunteers. The stimulus of 5 Hz sinusoidal electrical current was applied to the left index finger during 20s (with interval of 10s between stimuli) with intensity of twice the sensitivity threshold. To remove the stimulus artefacts and noises in the 8-10Hz band of frequency, the Second Order Blind Identification associated with Robust Orthogonalization (SOBI-RO) was applied. The EP estimated with 5 Hz stimulus presented the following components: N104 (one volunteer), P179 (four volunteers) and N234 (three volunteers), P280 (three volunteers) and N493 (all volunteers). The SOBI-RO techniques can be a very useful tool in artefacts and noise reduction on the EP estimation.

NON-INVASIVE REAL-TIME FETAL ECG EXTRACTION - A Block-on-Line DSP Implementation based on the JADE Algorithm

Danilo Pani, Silvia Muceli and Luigi Raffo

The possibility to access the fetal ECG non-invasively during the early stages of the pregnancy is a paramount requirement for cardiologists aiming to treat fetuses with congenital hearth diseases. Several research works have been presented during the past years to address this issue. In this paper we present a block-on-line blind source separation technique that combines the powerfulness of the batch JADE algorithm to the requirements of a separation able to adapt to a time-varying mixing process. To avoid estimated sources permutation, a simple preconditioning technique in conjunction with a proper parameters tuning has been developed and tested. The whole algorithm has been implemented on a powerful floating-point Digital Signal Processor, and it is ready to be embedded in an acquisition device for a deeper experimentation. Real-time performances have been assessed by means of a cycle accurate simulation.

SHORT-TERM CEPSTRAL ANALYSIS APPLIED TO VOCAL FOLD EDEMA DETECTION

Silvana Cunha Costa, Benedito G. Aguiar Neto, Joseana Macêdo Fechine and Menaka Muppa

Digital signal processing techniques have been used to perform an acoustic analysis for vocal quality assessment due to the simplicity and the non-invasive nature of the measurement procedures. Their employment is of special interest, as they can provide an objective diagnosis of pathological voices, and may be used as complementary tool in laryngoscope exams. The acoustic modeling of pathological voices is very important to discriminate normal and pathological voices. The degree of reliability and effectiveness of the discriminating process depends on the appropriate acoustic feature extraction. This paper aims at specifying and evaluating the acoustic features for vocal fold edema through a parametric modeling approach based on the resonant structure of the human speech production mechanism, and a nonparametric approach related to human auditory perception system. For this purpose, LPC and LPC-based cepstral coefficients, and mel-frequency cepstral coefficients are used. A vector-quantizing-trained distance classifier is used in the discrimination process.

DSP IMPLEMENTATION AND PERFORMANCES EVALUATION OF JPEG2000 WAVELET FILTERS

Ihsen Ben Hnia Gazzah, Chokri Souani and Kamel Besbes

The lifting scheme wavelet Transform allows efficiency implementation improvement over filter banks method. In this paper, we present results of a DSP implementation of Lifting scheme algorithm for 2D discrete wavelet transform (2D-DWT). The 5/3 and 9/7 filters have been used for decomposing and reconstructing images. We focus on the DSP memory use in order to optimize speed execution time. Implementation performances are compared when implementing the 9/7 and 5/3 filters into TMS320C6713. The implemented code is optimized in different ways especially within memory usage.

EARS: ELECTROMYOGRAPHICAL AUTOMATIC RECOGNITION OF SPEECH

Szu-Chen Stan Jou and Tanja Schultz

In this paper, we present our research on automatic speech recognition of the surface electromyographic signals that are generated by the human articulatory muscles. With parallel recorded audible speech and electromyographic signals, experiments are conducted to show the anticipatory behavior of electromyographic signals with respect to speech signals. Besides, we demonstrate how to develop phone-based speech recognizers with carefully designed electromyographic feature extraction methods. We show that articulatory feature (AF) classifiers can also benefit from the novel feature, which improve the F-score of the AF classifiers from 0.467 to 0.686. With a stream architecture, the AF classifiers are then integrated into the decoding framework. Overall, the word error rate improves from 86.8% to 29.9%.

OTOLITH IMAGE ANALYSIS BY COMPUTER VISION

Anatole Chessel, Ronan Fablet, Charles Kervrann and Frederic Cao

Otoliths are small stone located in fish inner ears and characterised by an accretionnary growth. They act as a biological archive and are of much use in marine biology and ecology. In this article a computer vision framework is presented which recover the successive shapes of the otolith and the significant ridges and valleys from a 2D grayscale image. Seeing vision processes as complex systems, an iterated process is presented using two perceptual information to drive a variational algorithm which considers the successive concentric shapes of the otoliths as level-sets of a dome shaped potential function. Potential applications includes in particular fish age estimation, otoliths morphogenesis modelling, otolith proxy fusion.

MULTIDIMENSIONAL POLYNOMIAL POWERS OF SIGMOID (PPS) WAVELET NEURAL NETWORKS

João Fernando Marar and Helder Coelho

The study of function approximation is motivated by the human limitation and inability to register and manipulate with exact precision the behavior variations of the physical nature of a phenomenon. Many real world problem can be formulated as function approximation problems and from the viewpoint of artificial neural networks these can be seen as the problem of searching for a mapping that establishes a relationship from an input to an output space through a process of network learning. A family of polynomial wavelets generated from powers of sigmoid functions is presented, in order to abolish restrictions of the backpropagation algorithm. We described how a multidimensional wavelet neural networks based on these functions can be constructed, trained and applied in pattern recognition tasks. As an example of application for the method proposed, it is studied the exclusive-or (XOR) problem

ADAPTATIVE SIGNAL SAMPLING AND SAMPLE QUANTIZATION FOR RESOURCE-CONSTRAINED STREAM PROCESSING

Deepak Turaga, Olivier Verscheure, Daby Sow and Lisa Amini

We propose a low-complexity encoding strategy for efficient compression of biomedical signals. At the heart of our approach is the combination of non-uniform signal sampling together with sample quantization to improve the source coding efficiency. We propose to jointly extract and quantize information (data samples) most relevant to the application processing the incoming data in the backend unit. The proposed joint sampling and quantization method maximizes a user-defined utility metric under system resource constraints such as maximum transmission rate or encoding computational complexity. We illustrate this optimization problem on electrocardiogram (ECG) signals, using the Percentage Root-mean-square Difference (PRD) metric as the utility function measuring the distortion between the original signal and its reconstructed (inverse quantization and linear interpolation) version. Experiments conducted on the MIT-BIH ECG corpus using the well-accepted {\em FAN} algorithm as the non-uniform sampling method show the effectiveness of our joint strategy: Same PRD as '{\em FAN} alone' at half the data rate for less than three times the (low) computational complexity of {\em FAN} alone.

SCREENING OF OBSTRUCTIVE SLEEP APNEA BY RR INTERVAL TIME SERIES USING A TIME SERIES NOVELTY DETECTION TECHNIQUE

Andre Lemos, Carlos Julio Tierra-Criollo and Walmir Caminhas

This work proposes a methodology to screen obstructive sleep apnea (OSA) based on RR interval time series using a time series novelty detection technique. Initially, the RR interval is modeled using an autoregressive model. Next, for each data point of the time series, the model output is compared with the observed value and the prediction error is generated. The prediction error is then processed in order to detect novelties. Finally, the novelties detected are associated with apnea events. This methodology was applied to the Computers in Cardiology sleep apnea test data and correctly classified 29 out of 30 cases (96.67%) of both OSA and normal subjects, and correctly identified the presence of apnea events in 14078 out of 17268 minutes (81.53%) of the test data set.

MRI SHOULDER COMPLEX SEGMENTATION AND CLASSIFICATION

Gabriela Pérez, J. F. Garamendi, R. Montes Diez and E. Schiavi

This paper deals with a segmentation (classification) problem which arises in the diagnostic and treatment of shoulder disorders. Classical techniques can be applied successfully to solve the binary problem but they do not provide a suitable method for the multiphase problem we consider. %%@ To this end we compare two different methods which have been applied successfully to other medical images modalities and structures. Our preliminary results suggest that a successful segmentation and classification has to be based on an hybrid method combining statistical and geometric information.

LEVEL SET BRAIN SEGMENTATION WITH AGENT CLUSTERING FOR INITIALISATION - Fast Level Set Based MRI Tissue Segmentation with Termite-Like Agent Clustering for Parameter Initialization

David Feltell and Li Bai

This paper presents a novel 3D brain segmentation method based on level sets and bio-inspired methodologies. Level set segmentation methods, although highly promising, require manual selection of seed positions and thereshold parameters, along with manual reinitialisation to a new level set surface for each candidate region. Here, the use of swarm intelligent mechanisms is used to provide all the statistical data and sample points required, allowing automatic initialisation of multiple level set solvers. This is shown by segmentation of white matter, grey matter and cerebro-spinal fluid in a simulated T1 MRI scan, followed by direct comparison between a commercial level application - FMRIB's FAST - and the ground truth anatomical model.

COMPUTERISED SYSTEM FOR EVALUATION OF ASYMMETRY OF POSTURAL PARAMETER COEFFICIENTS IN SCOLIOSES

Andrzej Dyszkiewicz, Zygmunt Wróbel and Józef Opara

Abstract Background: The work presents the clinical outline of the stature defects and scoliosis as well as the contemporary methodology of the thorax, spine and leg’s bone radiogram measurements. In order to increase the repeatability of the results and to create the computer records, which support the monitoring of the scoliosis, the algorithm for the process of the radiologic image was developed. It automatises the time consuming process of measuring and processing data by doctor. The image processing is initiated by an interactive procedure where key points of biological structures are marked with a cursor. Other measurements are done automatically. The algorithm is also an attempt to use the author’s modification of the measuring of the spine and thorax geometry, which increases precision when compared to the methods by Cobb, Fergusson and Gruca. Results of the radioplanimetric investigations compared with system for analysing the trajectory of respiratory motion and asymmetry weight distribution system in the foot. Design: clinical using Aim of the study: The aim of this compilation is a practical use of contemporary existing measurement methods of side curvature of the spine to construct practical algorithm and easy to use multipart software. Mathematical analysis of thorax and bone radiograms geometry combined with results of thorax trajectory movement enable the creations of individual patient symmetry indexes with the description of a disease monitoring. Subject: Patients suffering from thorax and spine trauma, hypertension, collagen and asthmatic disease, diabetes, taking vascular medication, having frostbites and after injury to upper extremity were excluded from the study. The examinations were carried out in the following group of patients: (1) examined group (A), consisted of 12 woman, average aged 32,9 ± 4,6 years and 8 men aged 34,7 ± 6,3 year, with right-thorax scoliosis; (2) control group (B), consisted of patients with normal spine (treated by gastric ill), 6 woman and 4 men, average age 35,7±5,8 years Intervention: In the first part the measurement algorithm assumes conducting geometrical measurements according to Cobb’s and Ferguson’s recommendation. Analyser of radiograms co-operates with spiromethr and appliance to evaluation of thorax trajectory in respiratory motion. Multiparameter of patients on the long-term observations significantly enables more accurate evaluation of a disease progression or regression. The researchers used a prototypical diagnostic device, consisting of 4 elastic tapes embracing the chest, connected with an analogue-digital converters enabling the transmission of data through a parallel port to the “respiratory path” software which allowed monitoring of oscillation motion of right and left lungs. Second device was a foot picture and tension scanner. System measured asymmetry coefficients: (1) CA - Cobb’s angle; (2) FA - Fergusson’s angle; (3) GA - Gruca’s angle; (3) LAF- lungs asymmetry factor; (4) BAF -breath asymmetry factor; (5) PAF - pelvic asymmetry factor; (6) FAF -foot asymmetry factor; (7) LCC - lung capacity coefficient. Results: Patients described by asymmetry coefficients CA, FA, GA, LAF, BAF, PAF, FAF, LCC) show completely different values in group of sick patients (A) and in control group (B). During the table 1 analysing we can clearly notice that in scoliosis the level of asymmetry of newly inserted coefficients LAF, BAF, PAF and FAF is comparative with coefficients leaned on Cobb’s, Fergusson’s and Gruca’s methods and clearly higher than coefficent leaned on LCC breath volume of lungs. Moreover it’s possible to see that LCC in group (A) don’t distinguish much from the value in group of healthy people (B). Conclusions: 1. The coefficients (LAF, PAF, FAF, CA, FA, GA, LCC) using in this investigations makes a possibility to differentiates a parameters healthy and scoliotic people clearly 2. New, plan-metric coefficients LAF, PAF, FAF (in scoliosis) have a good correlations with traditional, measured systems CA, FA, GA (Cobb, Fergusson, Gruca) 3. The plan-metric coefficients LAF, PAF, FAF, CA, FA, GA (in scoliosis) have a better correlations with breath asymmetry analysing factor BAF in comparison with traditional, spirometry test LCC

TOWARDS A UNIFIED MODEL FOR THE RETINA - Static vs Dynamic Integrate and Fire Models

Pedro Tomás, João Martins and Leonel Sousa

Many models have been proposed to describe the visual processing mechanisms in the retina. The spike generation mechanism of the models is typically performed by a Poisson process. Alternatively, a more realistic approach can be used by implementing an integrate and fire mechanism. In this paper we show that the Stochastic Leaky Integrate and Fire (SLIF) model is equivalent to a non-linear Poisson-based model. Furthermore, it proposes a dynamic model for the retina visual processing path, achieved through modulations. For estimating this model a two-step approach is proposed: (i) an initial estimation is computed by using a spike-triggered analysis, and (ii) the likelihood of the spike train is maximised by gradient ascent. An additionally a method is also presented to reduce the sharpness of the impulsive responses of filters in the model.

DESCRIBING CRYPTOBIOSIS AS A TIME BASED PROTECTION SYSTEM USING PETRI NETS

Bengt Carlsson, K. Ingemar Jönsson and Keith Clark

Cryptobiosis represents the state of a living organism when it shows no visible signs of metabolic life, but maintains a capacity to return to an active, metabolic life. This peculiar state, although known from a wide variety of organisms, has received little attention from a theoretically biology perspective. We propose a finite state machine, initiated by one or more input signals, computing a number of transition conditions during an induction phase, a dormancy phase and a reactivating phase of an organism undergoing cryptobiosis. A time based security model is also proposed where a critical condition for successful entering of a state of cryptobiosis is defined.

REALTIME NEOCORTICAL COLUMN VISUALIZATION

Pablo de Heras Ciechomski and Robin Mange

This paper presents a method for real-time rendering of a neocortical column in the mouse brain with 10000 individually simulated neurons, as implemented in the software GabrielStudio (TM). It also presents how the same system is used to create movie sequences of scripted camera keyframes for high resolution outputs. The current system is running on an SGI Altix Prism Extreme with 16 parallel graphics cards and a shared memory of 300 GB. Gabrielstudio works as a virtual microscope for computational neuro-scientists to analyze their simulations of neurons.

A DNA-INSPIRED ENCRYPTION METHODOLOGY FOR SECURE, MOBILE AD-HOC NETWORKS (MANET)

Harry C. Shaw and Sayed Hussein

Molecular biology models such as DNA evolution can provide a basis for proprietary architectures that achieve high degrees of diffusion and confusion and resistance to cryptanalysis. Proprietary encryption products can serve both large and small applications and can exist at both application and network level. This paper briefly outlines the basis of the proprietary encryption mechanism which uses the principles of DNA reproduction and steganography (hidden word cryptography) to produce confidential text. The foundation of the approach includes: organization of coded words and messages using base pairs organized into genes, an expandable genome consisting of DNA-based chromosome keys, and a DNA-based message encoding, reproduction, and evolution. Such encryption models provide “Security by Obscurity”.

RULE OPTIMIZING TECHNIQUE MOTIVATED BY HUMAN CONCEPT FORMATION

Fedja Hadzic and Tharam S. Dillon

When using machine learning techniques for data mining purposes one of the main requirements is that the learned rule set is represented in a comprehensible form. Simpler rules are preferred as they are expected to perform better on unseen data. At the same time the rules should be specific enough so that the misclassification rate is kept to a minimum. In this paper we present a rule optimizing technique motivated by the psychological studies of human concept learning. The technique allows for reasoning to happen at both higher levels of abstraction and lower level of detail in order to optimize the rule set. Information stored at the higher level allows for optimizing processes such as rule splitting, merging and deleting, while the information stored at the lower level allows for determining the attribute relevance for a particular rule. The attributes detected as irrelevant can be removed and the ones previously detected as irrelevant can be reintroduced if necessary. The method is evaluated on the rules extracted from publicly available real world datasets using different classifiers, and the results demonstrate the effectiveness of the presented rule optimizing technique.

DIMENSIONALITY REDUCTION FOR IMPROVED SOURCE SEPARATION IN FMRI DATA

Rudolph L. Mappus IV, David Minnen and Charles Lee Isbell Jr.

Functional magnetic resonance imaging (fMRI) captures brain activity by measuring the hemodynamic response. It is often used to associate specific brain activity with specific behavior or tasks. The analysis of fMRI scans seeks to recover this association by differentiating between task and non-task related activation and by spatially isolating brain activity. In this paper, we frame the association problem as a convolution of activation patterns. We project fMRI scans into a low dimensional space using manifold learning techniques. In this subspace, we transform the time course of each projected fMRI volume into the frequency domain. We use independent component analysis to discover task related activations. The combination of these methods discovers sources that show stronger correlation with the activation reference function than previous methods.

FULLY-AUTOMATED SEGMENTATION OF TUMOR AREAS IN TISSUE CONFOCAL IMAGES - Comparison between a Custom Unsupervised and a Supervised SVM Approach

Santa Di Cataldo, Elisa Ficarra and Enrico Macii

In this paper we present a fully-automated method for the detection of tumor areas in immunohistochemical confocal images. The image segmentation provided by the proposed technique allows quantitative protein activity evaluation on the target tumoral tissue disregarding tissue areas that are not affected by the pathology, such as connective tissue. The automatic method that is based on an innovative unsupervised clustering approach enables more accurate tissue segmentation with respect to traditional supervised methods that can be found in literature, such as Support Vector Machine (SVM). Experimental results conducted on a large set of heterogeneous immunohistochemical lung cancer images demonstrate that the proposed approach overcomes the performance of SVM by 8%, achieving an accuracy of 90% on average.

COGNITIVE STATE ESTIMATION FOR ADAPTIVE LEARNING SYSTEMS USING WEARABLE PHYSIOLOGICAL SENSORS

Aniket A. Vartak, Cali M. Fidopiastis, Denise M. Nicholson, Wasfy B. Mikhael and Dylan D. Schmorrow

This paper presents a historical overview of intelligent tutoring systems and describes an adaptive instructional architecture based upon current instructional and adaptive design theories. The goal of such an endeavor is to create a training system that can dynamically change training content and presentation based upon an individual’s real-time measure of cognitive state changes. An array of physiological sensors is used to estimate the cognitive state of the learner. This estimate then drives the adaptive mitigation strategy, which is used as a feed-back and changes the how the learning information is presented. The underlying assumptions are that real-time monitoring of the learners cognitive state and the subsequent adaptation of the system will maintain the learner in an overall state of optimal learning. The main issues concerning this approach are constructing cognitive state estimators from a multimodal array of physiological sensors and assessing initial baseline values, as well as changes in baseline. We discuss these issues in a data processing block wise structure, where the blocks include synchronization of different data streams, feature extraction, and forming a cognitive state metric by classification/clustering of the features. Initial results show our current capabilities of combining several data streams and determining baseline values. Given that this work is in its initial staged the work points to our ongoing research and future directions.

AN ECoG BASED BRAIN COMPUTER INTERFACE WITH SPATIALLY ADAPTED TIME-FREQUENCY PATTERNS

Nuri F. Ince, Fikri Goksu and Ahmed H. Tewfik

Recognition of motor imagery related brain activity in multichannel data is important for constructing brain-computer interfaces. However this task is also difficult due to high dimensionality and lack of prior knowledge of informative cortical areas. In this paper we describe an adaptive approach for the classification of multichannel electrocorticogram (ECoG) recordings for a Brain Computer Interface. In particular the proposed approach implements a time-frequency plane feature extraction strategy from multichannel ECoG signals by using a dual-tree undecimated wavelet transform. The dual-tree undecimated wavelet transform generates a redundant feature dictionary with different time-frequency resolutions. Rather than evaluating individual discrimination performance of each electrode or a candidate feature, the proposed approach implements a wrapper strategy to select a subset of features from the redundant structured dictionary by evaluating the classification performance of their combination. This enables the algorithm to evaluate the performance of candidate features coming from different cortical areas and/or time frequency locations. We show experimental classification results on the ECoG data set of BCI competition 2005. The proposed approach achieved a classification accuracy of 93% by using only three features. The results we obtained show that the proposed approach can be used in recognition of motor imagery events in EcOG with high accuracies.

BIOLOGICALLY INSPIRED BEAMFORMING WITH SMALL ACOUSTIC ARRAYS

Douglas Jones, Michael Lockwood, Bruce Wheeler and Albert Feng

Many biological hearing systems perform much better than existing signal processing systems in natural settings. Two biologically inspired adaptive beamformers, one mimicking the mammalian dual-delay-line localization system, show SNR gains in challenging cocktail-party scenes substantially exceeding those of conventional adaptive beamformers. A ``zero-aperture" acoustic vector sensor array inspired by the parasitoid fly {\it Ormia ochracea} and accompanying algorithms show even better performance in source recovery than the binaural beamformers, as well as the ability to localize multiple nonstationary sources to within two degrees. New experimental studies of the performance of the biologically inspired beamformers in reverberation show substantial reduction in performance in reverberant conditions that hardly affect human performance, thus indicating that the biologically inspired algorithms are still incomplete.

A NEW ALGORITHM FOR NAVIGATION BY SKYLIGHT BASED ON INSECT VISION

F. J. Smith

An insect can navigate accurately using the polarised light from a blue sky when the sun is obscured. They navigate using two different types of optical features: one is a set of three ocelli on the top of the head and the second is a celestial compass based on a relatively small set of photoreceptors on the dorsal rims of the compound eyes. Either feature can be used alone, but the ocelli appear to be less accurate than the dorsal rim receptors. Robots have been built that navigate using three photoreceptors, or three pairs of orthogonally oriented photoreceptors, but none has been designed which uses a full set of photoreceptors similar to those in the compound eyes, probably because they are not well understood. A model is proposed in this paper, based on a new physical representation of the dorsal rim compass, and four measured azimuths at whichand a simulation shows that this could provide an accurate navigational tool for a drone or robot, even in lightly clouded skies.

DETERMINE TASK DEMAND FROM BRAIN ACTIVITY

Matthias Honal and Tanja Schultz

Our society demands ubiquitous mobile devices that offer seamless interaction with everybody, everything, everywhere, at any given time. However, the effectiveness of these devices is limited due to their lack of situational awareness and sense for the users’ needs. To overcome this problem we develop intelligent transparent human-centered systems that sense, analyze, and interpret the user’s needs. We implemented learning approaches that derive the current task demand from the user’s brain activity by measuring the electroencephalogram. Using Support Vector Machines we can discriminate high versus low task demand with an accuracy of 92.2% in session dependent experiments, 87.1% in session independent experiments, and 80.0% in subject independent experiments. To make brain activity measurements less cumbersome, we built a comfortable headband with which we achieve 69% classification accuracy on the same task.

WEIGHTS CONVERGENCE AND SPIKES CORRELATION IN AN ADAPTIVE NEURAL NETWORK IMPLEMENTED ON VLSI

A. Daouzli, S. Saïghi, L. Buhry, Y. Bornat and S. Renaud

This paper presents simulations of a conductance-based neural network implemented on a mixed hardware-software simulation system. Synaptic connections follow a bio-realistic STDP rule. Neurons receive correlated input noise patterns, resulting in a weights convergence in a confined range of conductance values. The correlation of the output spike trains depends on the correlation degree of the input patterns.

STANDING JUMP LOFT TIME MEASUREMENT - An Acceleration based Method

Susana Palma, Hugo Silva, Hugo Gamboa and Pedro Mil-Homens

This paper describes two methods for the measurement of loft time in vertical jumps using signals from an acceleration sensor. The vertical jump accelerometer characteristic curve is presented and notable regions corresponding to key stages of the kinetic activity are identified. Using the accelerometer signals along three dimensions two different algorithms were devised to compute the loft time. These algorithms are based on the morphology of the signal. The first uses the the maximum value of the curve during the landing stage; the second uses the time interval between minimum and maximum values of the acceleration during the flight and landing stages, respectively. To validate these algorithms, a standard algorithm to compute the loft time from force platform signals was employed and these values taken as ground truth. Performance assessment was performed by computing the relative errors between the loft time determined from the force signal and the values obtained with each of the proposed approaches. Preliminary results for a set of 60 jumps let to relative errors of 7.0% for the first method and 2.9% for the second method.

USER TUNED FEATURE SELECTION IN KEYSTROKE DYNAMICS

Jyothi Bhaskarr Amarnadh, Hugo Gamboa and Ana Fred

In this paper, we present a new approach for user biometric verification based on keystroke dynamics. In our approach, the performance of simple classifiers (namely KNN and Bayes classifiers) is tested in a user tuned feature selection method, based on a open password approach. The impact of the training set size is studied, obtaining good results in a preliminary study on a population of 20 users.

EVALUATION OF NOVEL ALGORITHM FOR SEARCH OF SIGNAL COMPLEXES TO DESCRIBE COMPLEX FRACTIONATED ATRIAL ELECTROGRAM

V. Kremen and L. Lhotska

Complex fractionated atrial electrograms (CFAEs) represent the electrophysiologic substrate for atrial fibrillation (AF). Progress in signal processing algorithms to identify CFAEs sites is crucial for the development of AF ablation strategies. Individual signal complexes in CFAEs reflect electrical activity of electrophysiologic substrate at given time. We developed and tested a novel algorithm based on wavelet transform. This algorithm enables to find individual signal complexes in CFAEs automatically and based on that the CFAEs complexity can be described in a novel way. The method was tested using a representative set of 1.5s A-EGMs (n = 113) ranked by an expert into 4 categories: 1 - organized atrial activity; 2 - mild; 3 - intermediate; 4 - high degree of fractionation. Individual signal complexes were marked by an expert in every A-EGM in the dataset. This ranking was used as gold standard for comparison with the novel automatic search method. Achieved results indicate that use of appropriate level of wavelet signal decomposition could carry high level of predictive information about the state of electrophysiologic substrate for AF and is efficient to help to describe the level of complexity of CFAEs in a novel way.

CARDIAC BEAT DETECTOR - A Novel Analogue Circuitry for the First Heart Sound Discrimination

Shinichi Sato

Cardiac beat detector, which is an analogue circuitry installed in a novel non-invasive system for measuring heart rate in mice by using a piezoelectric transducer (PZT) sensor, performs an critical role in detecting the first heart sound (S1) in heart sounds. The PZT sensor detects heartbeat vibration and converts it to an electrical signal, namely the heart sounds. The measurement in intervals of S1s in the heart sounds is required to calculate heart rate, however, it is not simple because a S1 is a vibrating signal and has multiple peaks, which fluctuate in interval and in magnitude. In addition, respiration sound noise, which has frequency components similar with that of S1, makes S1 detection difficult and complex. The cardiac beat detector made it possible to overcome these problems by transforming multi-peaked S1 signal into a quasi-digital pulse. This technique is also available for the use in humans. Thus, the cardiac beat detector would contribute to the progress in the non-invasive heart rate measurement when it is installed in various, novel phonocardiogram-based equipments for the use in the fields of clinical and basic science in medicine.

INSECT SENSORY SYSTEMS INSPIRED COMMUNICATIONS AND COMPUTING (II): AN ENGINEERING PERSPECTIVE

Zhanshan (Sam) Ma, Axel W. Krings and Robert E. Hiromoto

In a previous paper, we reviewed the state-of-the-art research in the field of insect sensory systems in the context of their inspirations to communications and computing. We argued that, despite the extensive attention and progress in this interdisciplinary research of computer science and biology, the huge potential that insect sensory systems may help to inspire is far from being fully explored. In particular, the contrasting similarity between the nearly ubiquitous existence of insect communication networks in nature and the paradigm of pervasive computing has received little attention. For example, the chemosensory communication systems in many of the moths, ants and beetles populations are essentially "wireless" sensory networks. The difference between the "wireless" network of an insect population and the engineered wireless network is that the messages are encoded with semiochemicals (also known as infochemicals) in the case of insect population, rather than with radio frequencies. The computing node is the insect individual powered by its brain and sensory and neuromotor systems, rather than by the sensor powered by a microchip. While in the previous article, our discussion was focused on the biological systems, the present article discusses the engineering aspects of insect sensory systems inspired communications and computing, and suggests some promising research topics in perspective. The research topics we are interested in include: cellular computing and agent-based computing, wireless sensor networks, insect inspired robots, biosensoring, neural network modeling, dendritic neuronal computing, molecular networks, non-cooperative behaviours in social insects, etc.

DIFFERENCES IN PHYSIOLOGICAL RESPONSES TO THE INTENSITY OF MENTAL STRESS

Chi’e Soga, Chikamune Wada and Shinji Miyake

It is widely understood that mental stress produces various physiological changes. Though the relationship between mental stress and physiological feedback has been extensively reported, few reports have tried to clarify the relationships between physiological responses and the intensity level of stress. In this study, we investigated autonomic nervous system activities to find a physiological index based on which we can evaluate the intensity of mental stress. As a result, we found that there were different response patterns for each physiological index. We consider that each physiological index show different feelings or/and situation related to the intensity of mental stress.

IMPLEMENTING AN ARTIFICIAL CENTIPEDE CPG - Integrating Appendicular and Axial Movements of the Scolopendromorph Centipede

Rodrigo R. Braga, Zhijun Yang and Felipe M. G. França

In nature, a high number of species seems to have purely inhibitory neuronal networks called Central Pattern Generators (CPGs), allowing them to produce biological rhythmic patterns in the absence of any external input. It is believed that one of the mechanisms behind CPGs functioning is the Post-Inhibitory Rebound (PIR) effect. Based in the similarity between the PIR functioning and the Scheduled by Multiple Edge Reversal (SMER) distributed synchronizer algorithm, a generalized architecture for the construction of artificial CPGs was proposed. In this work, this architecture was generalized by integrating, in a single model, the axial and appendicular movements of a centipede in the fastest gait pattern of locomotion.

AN FPGA PLATFORM FOR REAL-TIME SIMULATION OF TISSUE DEFORMATION

Samson Ajagunmo and Aleksandar Jeremic

The simulation of soft tissue deformations has many practical uses in the medical field such as diagnosing medical conditions, training medical professionals and surgical planning. While there are many good computational models that are used in these simulations, carrying out the simulations is time consuming especially for large systems. This is mainly due to the fact that most of the simulators are software-based, implemented on general-purpose computers that are not optimized to carry out the operations needed for simulation. In order to improve the performance of these simulators, field-programmable-gate-arrays (FPGA) based accelerators for carrying out Matrix-by-Vector multiplications (MVM), the core operation required for simulation, have been proposed recently. A better approach yet, is to implement a full accelerator for carrying out all operations required for simulation on FPGA. In this paper we propose an FPGA accelerator tested for simulating soft-tissue deformation using finite-difference approximation of elastodynamics equations based on conjugate-gradient inversion of sparse matrices. The resource and timing requirements show that this approach can achieve speeds capable of carrying out real-time simulation.

AN INVERSE MODEL FOR LOCALIZATION OF LOW-DIFFUSIVITY REGIONS IN THE HEART USING ECG/MCG SENSOR ARRAYS

Ashraf Atalla and Aleksandar Jeremic

Cardiac activation and consequently performance of the heart can be severely affected by certain electrophysiological anomalies such as irregular patterns in the activation of the heart. Since the wavefront propagation occurs through the diffusion of ions Na+ and K+ the reduced mobility of ions can be equivalently represented as a reduction of ionic diffusivity causing irregularities in heartbeats. In this paper we propose models for the cardiac activation using inhomogeneous reaction-diffusion equations in the presence of diffusivity disorders. We also derive corresponding statistical signal processing algorithms for estimating (localizing) parameters describing these anomalies. We illustrate applicability of our techniques and demonstrate the identifiability of the parameters through numerical examples using a realistic geometry.

RELATIONSHIP BETWEEN THERMAL PERCEPTION AND MECHANICAL CHARACTERISTICS ON A PALM - Aiming at Developing a Communication Support Device for the Deaf-Blind

Chikamune Wada, Kuranosuke Sako and Hiroshi Horio

Our final goal is to develop a portable display which will enable the deaf-blind to character on the palm through the use of tactile sensations. We propose the use of thermal stimulation as the tactile sensation, because in this way small-sized and lightweight devices can be developed. However, it might still be impossible to capture continuous movement, which is necessary to recreate characters on the palm. In past research, we found that thermal perception is dependent on the palm position. Therefore, in this study, we investigated the cause of this position dependence by comparing the skin’s thermal perception and its mechanical characteristics.

UNVEILING INTRINSIC SIMILARITY - Application to Temporal Analysis of ECG

André Lourenço and Ana Fred

The representation of data in some visual form is one of the first steps in a data-mining process in order to gain some insight about its structure. We propose to explore well known visualization and unsupervised learning techniques, namely clustering, to improve the understanding about the data and to enhance possible relations or intrinsic similarity between patterns. Specifically, Multidimensional Scaling (MDS) and Clustering Ensemble Methods are exploited separately and combined to provide a clearer visualization of data organization. The presented methodology is used to improve the understanding of ECG signal acquired during Human Computer Interaction (HCI).