Ghasem Sadeghi Bajestani; Abbas Monzavi; Seyed Mohammad Reza Hashemi Golpayegani; Farah Ashrafzadeh
Volume 11, Issue 2 , June 2017, , Pages 167-185
Abstract
Autism spectrum disorder (ASD) is a common disorder among children which despite painstakingly effort, it is not yet possible to be precisely detected using paraclinical methods. On the other hand, early detection, before 18th month, has pivotal role in treatment procedure. In this study, we present ...
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Autism spectrum disorder (ASD) is a common disorder among children which despite painstakingly effort, it is not yet possible to be precisely detected using paraclinical methods. On the other hand, early detection, before 18th month, has pivotal role in treatment procedure. In this study, we present a method for early diagnosis of ASD based on the qualitative analysis of the Electroencephalogram (EEG) signal. We develop a new domain for quantifying the quality of interaction is present. We name it 'stretching – folding space’ (SFS). This domain is based on cybernetics, holistic and information-based analysis approaches. Therefore, it provides a non-deterministic approach to the biosignals. We collected data from 60 normal and 60 children with ASD in the range of 3-10 years old. We extracted features from the data in the SFS domain. The design of the study is self-controlled, meaning that each child serves as his/her own control. Each subject in the study watched a cartoon with and without sound, and the EEG signals were recorded. Statistical tests are applied on the extracted qualitative features in the SFS domain. The difference between the features of the data for each group (normal and ASD) was extracted, and the difference were compared between the groups. The results indicate that there is a statistically significant difference between the SFS features of normal and autism children. We conclude that our proposed method can serve as a new signal processing tool for diagnosing autism.
Hossein Banki-Koshki; Mohammad Tafazoli Shadpoor
Volume 10, Issue 1 , May 2016, , Pages 85-97
Abstract
Along with advancement in medical technologies, the academic field of Biomedical Engineering (BME) was developed. BME which was once considered as a subdivision of other disciplines, has gradually become an independent discipline with established departments. The extended medical and biological applications ...
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Along with advancement in medical technologies, the academic field of Biomedical Engineering (BME) was developed. BME which was once considered as a subdivision of other disciplines, has gradually become an independent discipline with established departments. The extended medical and biological applications of the new discipline resulted in itsrapid progress. It is essential for academic centers to examine novel education and research areas of biomedical engineering every few years. In this paper we presented educational and research status of biomedical engineering among world's 50 top universities from different continents. We used three world university rankings (Time, QS, CWUR) to select top universities in 2016. Overally we studied 17 universities from America, 19 universities from Europe and 14 universities from Asia and Oceania. The undergraduate and postgraduate educational programs were presented and the independency status of biomedical engineering departments were studied using four models and results were compared among universities from different continents. The foundation year and number of academic staff of BME departmentswere further shown.Moreover, the BME researchfiledswere shown and compared among top universities from different contients and the most prevalent research areas were presented.
Biomedical Image Processing / Medical Image Processing
Mahdie Ghasemi; Ali Mahloojifar; Mehdi Omidi
Volume 8, Issue 3 , September 2014, , Pages 261-275
Abstract
Functional changes in the brain motor network are responsible for the major clinical features of Parkinson’s disease (PD). Recent studies on investigation of the brain function show that there are spontaneous fluctuations between regions at rest as resting state network affected in various disorders. ...
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Functional changes in the brain motor network are responsible for the major clinical features of Parkinson’s disease (PD). Recent studies on investigation of the brain function show that there are spontaneous fluctuations between regions at rest as resting state network affected in various disorders. In this paper, we examine changes of functional dependency between brain regions of interest associated with known anatomical pathology in Parkinson Disease (PD) using copula theory on resting state fMRI. Five types of copulas were tested: Gaussian and t (Euclidean), Clayton, Gumbel and Frank (Archimedean). We used an efficient maximum likelihood procedure for estimating copula parameters. Goodness of fits was tested using root mean square error (RMSE) and kulback-leibler divergence between each copula function and joint empirical cumulative distribution. Control vs PD group comparison was also done on dependency parameter using parametric and nonparametric tests. The results show that functional dependency between cerebellum and basal ganglia is much stronger in PD than in control. In this paper, we proposed for the first time that joint distribution characteristics could potentially provide information on discriminative features for functional connectivity analysis between healthy and patients.
Hadi Borjkhani; Samad Sheikhaei; Mehdi Borjkhani
Volume 8, Issue 1 , March 2014, , Pages 31-43
Abstract
Currently need for ultra low power wireless transmitters in medical applications are inevitable. In this paper a new transmitter for body-worn and implantable sensor nodes is presented. Most of the sensor nodes supply their power using energy harvesting instead of a battery, since the power earned by ...
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Currently need for ultra low power wireless transmitters in medical applications are inevitable. In this paper a new transmitter for body-worn and implantable sensor nodes is presented. Most of the sensor nodes supply their power using energy harvesting instead of a battery, since the power earned by harvesting is limited, so the average and the peak power consumption of the sensor node must be minimized. Transmitter blocks which implemented in sensor nodes are too power consuming. So a new low power Binary Frequency Shift Keying (BFSK) transmitter based on sub-harmonic current mode injection locking, and edge combining technique has been proposed. The proposed transmitter was designed to make a mutual communication between sensor node and base station, so there is no need for complexity at receiver side. In order to reduce the consuming power at transmitter side, BFSK modulation is done at reference frequency to prevent usage of power consuming low phase noise oscillator at carrier frequency. A 34MHz reference clock is used and the frequency of reference clock multiplied by 12 for desired carrier frequency. The phase noise of the carrier at 1MHz frequency offset is -117 dBc/Hz. Total power consumption of the transmitter is about 144μW. The output carrier frequency is 408MHz. BFSK modulation scheme is used at the frequency much lower than the carrier frequency in order to reduce the power consumption.
Biomedical Image Processing / Medical Image Processing
Malihe Miri; Mohammad Taghi Sadeghi; Vahid Abootalebi
Volume 8, Issue 1 , March 2014, , Pages 45-56
Abstract
Successful outcomes of Sparse Representation-based Classifier (SRC) and Sparse Subspace Clustering (SSC) in many applications motivated us to combine these methods and propose a hierarchical classifier. The main idea behind the SRC and SSC algorithms is to represent a data using a sparse linear combination ...
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Successful outcomes of Sparse Representation-based Classifier (SRC) and Sparse Subspace Clustering (SSC) in many applications motivated us to combine these methods and propose a hierarchical classifier. The main idea behind the SRC and SSC algorithms is to represent a data using a sparse linear combination of elementary signals so that those elementary signals which are similar to the data contribute mainly in the representation. In this paper, the performance of a sparse representation based classifier is improved by pre-clustering of training samples using the SSC algorithm. A twostage SRC is then designed using the resulting clusters. A test data is classified by first determining the most similar cluster. The data label is subsequently found using the second stage classifier. The performance of the proposed method is evaluated considering cancer classification problem using the 14-Tumors microarray dataset. Due to low number of data samples per each class and high dimensionality of the data, this is a challenging problem. Curse of dimensionality, overfitting of the classifier to the training data and computational complexity are the possible related problems. Our experimental results show that the proposed method outperforms some other state of the art classifiers.
Biomedical Image Processing / Medical Image Processing
Mohammad Reza Rezaeian; Gholam Ali Hossein-Zadeh; Hamid Soltanian Zadeh
Volume 8, Issue 1 , March 2014, , Pages 87-99
Abstract
Chemical exchange saturation transfer (CEST) is a new mechanism of contrast generation in magnetic resonance imaging (MRI) which differentiates molecule biomarkers via chemical shift. CEST MRI contrast mechanism is very complex and depends on radio frequency (RF) power and RF pulse shape. Two approaches ...
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Chemical exchange saturation transfer (CEST) is a new mechanism of contrast generation in magnetic resonance imaging (MRI) which differentiates molecule biomarkers via chemical shift. CEST MRI contrast mechanism is very complex and depends on radio frequency (RF) power and RF pulse shape. Two approaches have been used to saturate contrast agent (CA) protons: continuous wave CEST (CW-CEST) and pulsed CEST. To find the optimal RF pulse, numerical solution of Bloch-McConnell equations (BME) may be used. In this paperwe find the optimum values of RF pulse parameters that maximize the CEST contrast. Discrete pulses have lower specific absorption ratio (SAR) than CW RF pulses. However, since discretization is performed on continuous RF pulses, optimizing the continuous RF pulses leads to the optimization of discrete RF pulses. Therefore, in this paper, Rectangular, Gaussian and Fermi pulses are investigated as CW RF pulses. In this investigation, in addition to considering the SAR limitation, 60 dB approximation for the RF pulse amplitude is used. To compare the efficiency of pulses, their resultant flip angles (FA) are assumed equal. Efficiency of CW-CEST is investigated using two parameters, CEST ratio and SAR. According to these parametres, rectangular, Fermi and Gaussian RF pulses have the best performance respectively. Since implementation of rectangular RF is harder than Gaussian and Fermi RF pulses, Fermi and Gaussian RF pulses are desired. Our results suggest that it is possible to maximize CEST ratio by optimizing parameters of rectangular (with an amplitude of 5.7μT), Gaussian (σ about 0.7s) and Fermi (a-value about 0.3s) pulses. Results are verified by empirical formulation of CEST ratio.
Cell Biomechanics / Cell Mechanics / Mechanobiology
Seyed Hojat Sabzpoushan; Zahra Daneshparvar
Volume 7, Issue 3 , June 2013, , Pages 187-200
Abstract
The study of cardiac arrhythmia is a great help for prevention of the major reason of human death. To study the arrhythmias, we need cell models that not only mimic AP’s normal behavior, but also show their abnormal activity. The usual electrophysiological models contain a lot of details and hence ...
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The study of cardiac arrhythmia is a great help for prevention of the major reason of human death. To study the arrhythmias, we need cell models that not only mimic AP’s normal behavior, but also show their abnormal activity. The usual electrophysiological models contain a lot of details and hence complicate mathematics which lowers the computational efficiency. In this paper, a minimal 2-state variables model is presented that not only simulates normal characteristics of human ventricular cells like excitability, AP morphology, restitution and effects of currents block, but also replicates early after depolarization (EAD) which is an abnormal activity of cardiac cells. The presented model is a conductance based one, incorporating two currents; inward and outward that delighting all the membrane inward and outward currents respectively. The adjustment and regulation of parameters were performed using an iterative algorithm that minimizes mean squares error between model responses and real APs. The effective range of parameters for initiation of the EAD is determined by the use of dynamical system analysis theory. The simulation results are in agreement with electrophysiological realities. The computing time of the model for an one-dimensional array of 10 cells is estimated to be between 34 to 112 times faster than some well-known electrophysiological models.
Neda Kaboodvand; Farzad Towhidkhah; Behzad Iravani; Shahriar Gharibzadeh
Volume 7, Issue 4 , June 2013, , Pages 297-310
Abstract
The central nervous system (CNS) uses a redundant set of joints and muscles to ensure both flexible and stable movements. How the CNS faces the complexity of control problem is not still clear. Modular control is one of the most attractive hypotheses in motor control. In this hypothesis, some motor primitives ...
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The central nervous system (CNS) uses a redundant set of joints and muscles to ensure both flexible and stable movements. How the CNS faces the complexity of control problem is not still clear. Modular control is one of the most attractive hypotheses in motor control. In this hypothesis, some motor primitives (e.g. muscle synergies) are considered as the building blocks that can be combined to present a vast repertoire of movements. EMG signals are required for extracting muscle synergies and NMF (nonnegative matrix factorization) is one of the most accepted methods for extracting synergies. Due to tonic component elimination of EMG signals involved in reaching movements in vertical planes, the standard NMF method is not applicable to extract muscle synergies. In this paper a modified NMF method, so-called semi-NMF, is applied to resolve the tonic component problem. On the other hand, to improve the accuracy of synergies' estimation and to find the global optimum for the optimization problem, we have proposed using HALS method. The proposed algorithm was applied to the experimental EMG recorded in arm reaching movement in the frontal plane. The results showed a good improvement both in accuracy and repeatability of extracted synergies. In addition, extracted muscle synergies were physiologically interpretable.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Mehdi Abdossalehi; Ali Motie Nasrabadi; Seyed Mohammad Firouzabadi
Volume 7, Issue 2 , June 2013, , Pages 143-153
Abstract
In this study, electroencephalogram (EEG) signals have been analyzed in positive, negative and neutral emotions. Here it is supposed that the brain has different independent sources during an emotional activity which will be extractable by Independent Component Analysis (ICA) algorithm. For resolving ...
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In this study, electroencephalogram (EEG) signals have been analyzed in positive, negative and neutral emotions. Here it is supposed that the brain has different independent sources during an emotional activity which will be extractable by Independent Component Analysis (ICA) algorithm. For resolving the illposeness problem of extracted components by ICA algorithm, first these sources were sorted by Shannon entropy and then the features of Katz fractal dimension and the first local minimum of the mutual information based on the time delay (tau) have been extracted for representing determinism. The results show that the determinism ratio of the sorted sources has significant difference during the time in three emotional states: positive, negative and neutral. The determinism ratio increases in neutral, negative and positive emotional states, respectively.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Amin Janghorbani; Mohammad Hasan Moradi; Abdollah Arasteh
Volume 7, Issue 2 , June 2013, , Pages 163-174
Abstract
Acute hypotension episodes (AHEs) are one of the hemodynamic instabilities with high mortality rate that is frequent among many groups of patients. Prognosis of acute hypotension episodes can help clinicians to diagnose the cause of this physiological disorder and select proper treatment based on this ...
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Acute hypotension episodes (AHEs) are one of the hemodynamic instabilities with high mortality rate that is frequent among many groups of patients. Prognosis of acute hypotension episodes can help clinicians to diagnose the cause of this physiological disorder and select proper treatment based on this diagnosis. In this study two groups of features, physiological and chaotic features, were extracted from the physiological time series to be applied for prediction of AHEs in the future 1 hour time interval. The best set of the features from the extracted features were selected using Genetic Algorithm (GA) and were classified by SVM. The prediction accuracy for physiological features was 87.5% and for chaotic features was 85%. In order to improve prediction accuracy, physiological and chaotic features were employed simultaneously in feature selection and the best combination of these features was selected by GA and classified by SVM. The best prognosis accuracy, which was achieved in this study by classification of the selected features, was 95% that was better than other previously studies on the same database.
Zahra sadat Hosseini; Mohadese Arabgari; Ali Farmad; Leili Goldoozian; Hamid Reza Maghari; Sara Aghajari; Edmond Zahedi
Volume 7, Issue 3 , June 2013, , Pages 277-285
Abstract
In this article a wireless patient monitoring system for vital signs (respiratory rate and heart beat) is presented. The recorded biosignal is the photoplethysmogram using a probe attached to the patient's finger. This signal is amplified, filtered and digitized by an on-board processor unit before finally ...
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In this article a wireless patient monitoring system for vital signs (respiratory rate and heart beat) is presented. The recorded biosignal is the photoplethysmogram using a probe attached to the patient's finger. This signal is amplified, filtered and digitized by an on-board processor unit before finally being sent wirelessly via a transmitter. The capacity of the current system is 16 patients whose data can be received through a common receiver by a central server which measures and displays the heart beat and respiratory rate for each patient on the monitor.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Ramtin Zargari Marandi; Seyed Hojat Sabzpoushan
Volume 6, Issue 4 , June 2012, , Pages 279-285
Abstract
Recent research in pervasive computing field leads to use of novel techniques for human activity recognition. One of these techniques is electrooculography which helps to record eye movements and by analyzing these movements’ patterns it’s possible to recognize daily life activities like ...
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Recent research in pervasive computing field leads to use of novel techniques for human activity recognition. One of these techniques is electrooculography which helps to record eye movements and by analyzing these movements’ patterns it’s possible to recognize daily life activities like reading. Eye movement patterns during reading can be detected using only EOG signals from horizontal channel instead of both horizontal and vertical channels, so only horizontal channel electrode placement on subject’s face set up for hindrance reduction is used in this work. Despite of channels reduction and by using DTW-based string matching algorithm and reading reference template extraction using wavelet transform and encoding of EOG signal, the performance of classification between reading and non-reading data increased, As it shows 4% increase in maximum recognition rate and also low standard deviation in recognition rate in addition to 7% increase in mean of recall which demonstrate that the algorithm is more robust and reliable in comparison with previous algorithms encountering various situations and subjects.
Biomedical Image Processing / Medical Image Processing
Amin Mohammadian; Hasan Aghaeinia; Farzad Towhidkhah
Volume 6, Issue 3 , June 2012, , Pages 207-218
Abstract
In this paper, a method is proposed based on the prior knowledge from a new subject to improve the performance of person-independent facial expression recognition. First, in order to obtain a basic system, a combination of geometric features and texture descriptor is compared with global features (i.e., ...
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In this paper, a method is proposed based on the prior knowledge from a new subject to improve the performance of person-independent facial expression recognition. First, in order to obtain a basic system, a combination of geometric features and texture descriptor is compared with global features (i.e., mapped face images using the Kernel-PCA and raw data of face images). The results of comparison under noisy conditions were investigated and evaluated by person-dependent/independent cross-validation method. The obtained basic system was evaluated by leave-one-subject-out cross-validation. Since the same subjects are not introduced in both training and test phases, the basic recognition system is person-independent and its performance is substantially lower than that of person-dependent cross-validation case. To improve the performance of the basic system, a method is proposed in which virtual samples are generated based on the prior knowledge from the new subject and are used in learning process. The results show that the recognition rate increases up to 96% for the person-dependent basic system, kernel-PCA method is more sensitive than the others to interpersonal variability, and the recognition rate is significantly (P<0.05) improved up to 91.39% compared to that of person-independent case.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Reza Soleimani; Seyed Mpjtaba Rouhani
Volume 5, Issue 2 , June 2011, , Pages 89-103
Abstract
in this paper, a novel and effective algorithm for classification of important heart arrhythmia is presented. The proposed algorithm uses heart rate variation (HRV) signal which has better chaotic characteristics. In addition to commonly used linear time domain and frequency domain features, nonlinear ...
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in this paper, a novel and effective algorithm for classification of important heart arrhythmia is presented. The proposed algorithm uses heart rate variation (HRV) signal which has better chaotic characteristics. In addition to commonly used linear time domain and frequency domain features, nonlinear (chaotic) features are examined, too. To increase classification accuracy and facilitate learning, two techniques are used: a) extracted features are reduced by generalized discriminant analysis (GDA) and b) by a self organizing map (SOM), the most informant data are selected. Chaotic features help to improve diagnosis accuracy from 92% up to 97%. The results indicate the importance of GDA and SOM in efficiency of proposed algorithm. MLP, SVM and PNN classifiers are examined and compared. The proposed algorithm was able to diagnose 7 arrhythmias PVC, AFL, AF, CHB, LBBB, VF, VT and normal sinus rhythm (NSR) with 97.4% accuracy.
Neuro-Muscular Engineering
Ali Falaki; Farzad Towhidkhah
Volume 5, Issue 2 , June 2011, , Pages 127-141
Abstract
Based on previous studies, human motor control system may apply two control strategies, impedance control and model based control, for learning motor skills and counteracting environmental instabilities. Since interaction among these controllers is not fully studied, the investigation of impedance and ...
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Based on previous studies, human motor control system may apply two control strategies, impedance control and model based control, for learning motor skills and counteracting environmental instabilities. Since interaction among these controllers is not fully studied, the investigation of impedance and model based controllers function during learning period seems desirable. In this study a supervisory controller was used to coordinate the model based and impedance controllers. Coordinating model based controller and impedance controller by using supervisory unit will result in simultaneously adjustment of forward motor command and joint stiffness. In order to evaluate performance of the suggested model, it was applied to arm reaching movements in the presence of external force fields. Results showed that both suitable impedance values and a proper internal model are required to fulfill movements similar to those of humans under different circumstances. Research has shown that central nervous system is able to purposefully modulate arm impedance to counteract environmental disturbances. This study showed that beside this modulation, the maximum motor learning may occur in direction with the least impedance and the most kinematic error. It also concluded that confronting abrupt changes in disturbance, the system managed to decrease error without learning the new dynamic using previous knowledge by supervisory system. A part of this compensation is due to stiffness variations and another part is due to decreasing the influence of model based controller.
Neuro-Muscular Engineering
Rahele Shafaei; Seyed Mohammad Reza Hashemi Golpayegani
Volume 5, Issue 3 , June 2011, , Pages 214-228
Abstract
One of main the issues in achieving to a successful FES control is using an as much as possible accurate model of the under electrical stimulation system so that it can adequately indicate the system behavior. Classical computational models that are commonly used for this purpose have a reductionism ...
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One of main the issues in achieving to a successful FES control is using an as much as possible accurate model of the under electrical stimulation system so that it can adequately indicate the system behavior. Classical computational models that are commonly used for this purpose have a reductionism nature; so they cannot consider the interaction existed in biological systems. Considering these restrictions, recently behavioral black box models are mostly used. These models focus on input/output dynamic, which is certainly the necessary modeling information for control design; thus the system is dealt with as a whole, which has hidden the interactions between components inside. Such a model has notbeen presented for elbow angle movement so far. Therefore in this study, we have been to present and verify a black box model of elbow joint movement in the transverse plane, forreaching movement control in people with C5/C6 SCI using dynamic neural networks, including time-delayed feedforward and recurrent networks. Extreme flexibility of time-delayed feedforward architectures was obtainedin a 2 layer structure including 5 hidden neurons and using 1.25s of history of input with performance indexes of 89.89% & 4.85% for cross correlation coefficient and normalized mean square error respectively. The best recurrent network with NARX architecture and equal history of input & output was also occurred in a 2 layer structure having 12 neurons in the hidden layer and using 0.1s of history, with performance indexes of 89.89% & 4.85% for cross correlation coefficient and normalized mean square error respectively. Comparison between best results of training using feedforward and recurrent networks, clearly illustrates both qualitative and quantitative excellency of the latter one in identification of the under-study system.
Rehabilitation Engineering
Diako Mardanbeigi; Mohammad Reza Mallakzadeh
Volume 4, Issue 4 , June 2010, , Pages 267-278
Abstract
This paper investigates prototyping an online, low-cost, video based and applicable eye tracker, which is called "Dias Eye Tracker". Disabled people can use the proposed system to communicate with computer. What have made the system different from the other low-cost eye trackers, are the accuracy of ...
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This paper investigates prototyping an online, low-cost, video based and applicable eye tracker, which is called "Dias Eye Tracker". Disabled people can use the proposed system to communicate with computer. What have made the system different from the other low-cost eye trackers, are the accuracy of gaze estimation, the different application parts of the software and the lightweight wireless hardware, which can be mounted on the user’s head. This paper introduces the software/hardware and the methods of the system. In addition, two methods of pupil tracking have been compared together, and an uncertainty analysis on the mapping function of the system has been done. The performance of the designed eye tracker has been evaluated by analyzing the answers to the three questionnaires, which were filled by disabled people after performing three specific tasks. The results show that the system performs well for interaction with computer.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Faride Ebrahimi; Mohammad Mikaili
Volume 4, Issue 2 , June 2010, , Pages 97-108
Abstract
Different biological signals including EEG, EOG, and EMG are recorded in sleep labs to diagnose sleep disorders. Data recorded during sleep is usually analyzed by sleep specialists visually. Since the sleep data is usually recorded for a long time period- namely a whole night- its visual inspection and ...
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Different biological signals including EEG, EOG, and EMG are recorded in sleep labs to diagnose sleep disorders. Data recorded during sleep is usually analyzed by sleep specialists visually. Since the sleep data is usually recorded for a long time period- namely a whole night- its visual inspection and classification is a very demanding and time consuming task so automatic analysis can definitely facilitate that. The key to automatic sleep staging is to extract suitable features. In the current study two classes of features are extracted from EEG signal. The first group is the features calculated from the coefficients of wavelet packet transformation (WPT) and the second group consists of a number of frequency features and a time feature, the amplitude of EEG signal itself. These two sets of features were separately mapped on a two dimensional space by SOM neural networks. The mappings indicated that these features are highly discriminative in separating sleep stages automatically. The data extracted from awake and deep sleep EEGs were mapped on two totally different regions. The mapping also indicated that EEG signal is not enough to separate stages thoroughly, as extracted data from EEG during REM and the first stage of NREM are mapped on the same region. Data extracted from EEG signals in the second stage overlapped with other stages which are in agreement with physiological definition of sleep stages.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Isar Nejadgholi; Mohammad Hasan Moradi; Fateme Abdol Ali
Volume 4, Issue 4 , June 2010, , Pages 279-292
Abstract
Many methods for automatic heartbeat classification have been applied and reported in literature, but relatively little number of them concerned with patient independent classification because of the less significant results compared to patient dependent ones. In this work, Reconstructed Phase Space ...
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Many methods for automatic heartbeat classification have been applied and reported in literature, but relatively little number of them concerned with patient independent classification because of the less significant results compared to patient dependent ones. In this work, Reconstructed Phase Space (RPS) theory is used to classify five heartbeat types (Normal, PVC, LBBB, RBBB and PB). In the first and second method, RPS is modeled by the Gaussian mixture model (GMM) and bins, respectively and then classified by classic Bayesian classifier. In the third method, RPS is directly used to train predictor time-delayed neural networks (TDNN) and classified based on minimum prediction error. All three methods highly outperform the results reported before for patient independent heartbeat classification. The best result is achieved using GMM-Bayes method with 92.5% accuracy for patient independent classification.
Zahra Amini; Vahid Abootalebi; Mohammad Taghi Sadeghi
Volume 4, Issue 4 , June 2010, , Pages 293-306
Abstract
The aim of this paper is to design a pattern recognition based system to detect P300 component in multi-channel electroencephalogram (EEG) trials. This system has two main blocks, feature extraction and classification. In feature extraction block, in addition to conventional features namely morphological, ...
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The aim of this paper is to design a pattern recognition based system to detect P300 component in multi-channel electroencephalogram (EEG) trials. This system has two main blocks, feature extraction and classification. In feature extraction block, in addition to conventional features namely morphological, frequency and wavelet features, some new features included intelligent segmentation, common spatial pattern (CSP) and combined features (CSP + Segmentation) have also been used. Three criteria were used for evaluation and selection of a feature set by choosing a subset of the original features that contains most of essential information. Firstly, a statistical analysis has been applied for evaluating the fitness of each feature in discriminating between target and non target signals. Secondly, each of these six groups of features was evaluated by a Linear Discriminant Analysis (LDA) classifier. Furthermore by using Stepwise Linear Discriminant Analysis (SWLDA), the best set of features was selected. Among these six feature vectors, intelligent segmentation was seen to be most efficient in classification of these signals. In classification phase, two linear classifiers -LDA and SWLDA- were used. The algorithm was described here has tested with dataset II from the BCI competition 2005. In this research, the best result for P300 detection is 97.05% .This result have proven to be more accurate than the results of previous works carried out in this filed.
Biomimetics
Saeed Rashidi; Seyed Mohammad Reza Hashemi Golpayegani; Ali Fallah; Farzad Towhidkhah
Volume 4, Issue 1 , June 2010, , Pages 33-44
Abstract
In drawing movements, the constraints imposed on the trajectory geometry properties and kinematics are known with two laws: 2/3 power law and isochrony phenomenon. In this paper experiments have been designed to study the relation between two empirical laws in straight and curved patterns of drawing ...
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In drawing movements, the constraints imposed on the trajectory geometry properties and kinematics are known with two laws: 2/3 power law and isochrony phenomenon. In this paper experiments have been designed to study the relation between two empirical laws in straight and curved patterns of drawing movements in 16-18 years old subjects. Providing two models of power is indicated that in drawing movements, invariant features can be defining. These features are independent of subject, direction and size of trajectory and together they can simplify the role of the upper motor control system and decrease the degrees of freedom and the computational complexity.
Zohre Dehghani Bidgoli; Mohammad Hossein Miranbaygi; Rasoul Malekfar; Ehsanollah Kabir; Tahere Khamechian
Volume 4, Issue 4 , June 2010, , Pages 307-316
Abstract
In this research, we investigated cancerous tissues from several organs of the human body using Raman spectroscopy. Different specimens with different pathologic labels (normal & cancerous) were borrowed from a pathology laboratory, and were investigated using two different Raman spectroscopy systems. ...
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In this research, we investigated cancerous tissues from several organs of the human body using Raman spectroscopy. Different specimens with different pathologic labels (normal & cancerous) were borrowed from a pathology laboratory, and were investigated using two different Raman spectroscopy systems. Since one of the goals of this investigation was detection of cancer, independent of type of the system, we introduced some algorithms for removing systemic differences from the spectra. Then we removed noise and fluorescence signals using a new wavelet created with LWT. The best classification result was 83% in differentiating between normal and cancerous specimens using the SVM classifier
Saeed Rashidi; Ali Fallah; Farzad Towhidkhah
Volume 4, Issue 2 , June 2010, , Pages 135-148
Abstract
Many methods are introduced for estimating the similarities or differences of time signals. One of theses methods, DTW algorithm, is also a utility for other domains including classification, data mining and matching regions between two time signals. DTW algorithm minimizes points distance between two ...
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Many methods are introduced for estimating the similarities or differences of time signals. One of theses methods, DTW algorithm, is also a utility for other domains including classification, data mining and matching regions between two time signals. DTW algorithm minimizes points distance between two signals by contracting or expanding the time axes to find the corresponding points. In this paper, with modification of the local constraints in DTW, a powerful method is proposed for measuring the global or local similarities between two signals. In addition to increasing the accuracy of signals distance measurements and decreasing the classification error, proposed algorithm is more stable than classic DTW against variations of structure and time signal source. The proposed method for dynamic signature verification was applied to a dataset of signatures from Turkish, Chinese and English people. The results of the experiments based on Fisher, Parzen Window and Support Vectors Machine classifications, showed that equal error rate (EER) is 1.46% and 3.51% with universal threshold for random and skilled forgeries, respectively.
Saeed Rashidi; Ali Fallah; Farzad Towhidkhah
Volume 4, Issue 3 , June 2010, , Pages 219-230
Abstract
Nowadays, fast and accurate algorithms for signature verification are very attractive. In the area of dynamic signature verification, the features are classified into two groups: parametric and functional features. In parametric algorithms, although the speed of features extraction and classification ...
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Nowadays, fast and accurate algorithms for signature verification are very attractive. In the area of dynamic signature verification, the features are classified into two groups: parametric and functional features. In parametric algorithms, although the speed of features extraction and classification process is faster than function based approaches but they are less accurate. The goal of this paper is modeling of the velocity signal that its pattern and properties are stable for a person. With using pole-zero models based on discrete cosine transform, a precise method is proposed for modeling and then features are extracted from strokes. These features are the deference of pole angles of strokes. Applying linear, parzen window and support vector machine classifiers, the proposed algorithm was tested on data set from Persian, Chinese, English and Turkish people and with common threshold, resulted equal error rates of 1.25% and 1.78% in the random and skilled forgeries, respectively.
Rehabilitation Engineering
Vahab Nekoukar; Abbas Erfanian Omidvar
Volume 4, Issue 4 , June 2010, , Pages 327-336
Abstract
One major limitation of walker-supported walking using functional electrical stimulation (FES) in paraplegic subjects is the high energy expenditure and the high upper body effort. Paraplegics should exert high amount of hand force to stabilize the body posture and to compensate lack of the sufficient ...
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One major limitation of walker-supported walking using functional electrical stimulation (FES) in paraplegic subjects is the high energy expenditure and the high upper body effort. Paraplegics should exert high amount of hand force to stabilize the body posture and to compensate lack of the sufficient torques at the lower extremity joints. In this paper, we introduce a 2-D musculoskeletal model of walker-assisted FES-supported walking of paraplegics. Using the developed model and an optimal controller, the stimulation patterns are determined such that the tracking errors of lower joint reference trajectories are minimized and the muscle activations and the handle reaction force (HRF) are reduced. Outputs of the optimal controller are stimulation patterns of the lower body muscles and torque acting on the upper body joints. The results show that the HRF and ground reaction force (GRF) generated by simulation are in agreement with the measured HRF and GRF. Moreover, the results indicate that the simulation-generated stimulation patterns of lower body muscles are in consist with the stimulation patterns reported in the literatures.
