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Implementation of a Biometric Interface in Voice Controlled Wheelchairs

Sound & Vibration 2020年第1期54卷 1-15页

作者： Lamia Bouafif Noureddine Ellouze National Institute of Biomedical Studies of Tunis 1092TunisTunisia Image and Signal Processing Laboratory ENIT BP 37University of Tunis El Manar1064Tunisia

In order to assist physically handicapped persons in their movements,we developed an embedded isolated word speech recognition system(ASR)applied to voice control of smart ***,in spite of the existence in the industrial market of several kinds of electric wheelchairs,the problem remains the need to manually control this device by hand via joystick;which limits their use especially by people with severe ***,a significant number of disabled people cannot use a standard electric wheelchair or drive it with *** proposed solution is to use the voice to control and drive the wheelchair instead of classical *** intelligent chair is equipped with an obstacle detection system consisting of ultrasonic sensors,a moving navigation algorithm and a speech acquisition and recognition module for voice control embedded in a DSP *** ASR architecture consists of two main *** first one is the speech parameterization module(features extraction)and the second module is the classifier which identifies the speech and generates the control word to motors power *** training and recognition phases are based on Hidden Markov Models(HMM),K-means,Baum-Welch and Viterbi *** database consists of 39 isolated speaker words(13 words pronounced 3 times under different environments and conditions).The simulations are tested under Matlab environment and the real-time implementation is performed by C language with code composer studio embedded in a TMS 320 C6416 DSP *** results and experiments obtained gave promising recognition ratio and accuracy around 99%in clean ***,the system accuracy decreases considerably in noisy environments,especially for SNR values below 5 dB(in street:78%,in factory:52%).

关键词： Handicapped persons smart wheelchairs voice control speech recognition training and classification

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Speech-Music-Noise Discrimination in Sound Indexing of Multimedia Documents

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Sound & Vibration 2018年第6期52卷 2-10页

作者： Lamia Bouafif Noureddine Ellouze National Institute of Biomedical Studies of Tunis 1092TunisTunisia Image and Signal Processing Laboratory ENIT BP 37University of Tunis El Manar1064Tunisia

Sound indexing and segmentation of digital documentsespecially in the internet and digital libraries are very useful tosimplify and to accelerate the multimedia document retrieval. Wecan imagine that we can extract multimedia files not only bykeywords but also by speech semantic contents. The maindifficulty of this operation is the parameterization and modellingof the sound track and the discrimination of the speech, musicand noise segments. In this paper, we will present aSpeech/Music/Noise indexing interface designed for audiodiscrimination in multimedia documents. The program uses astatistical method based on ANN and HMM classifiers. After preemphasisand segmentation, the audio segments are analysed bythe cepstral acoustic analysis method. The developed system wasevaluated on a database constituted of music songs with Arabicspeech segments under several noisy environments.

关键词： Speech processing audio indexing training andrecognition

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Seizure Prediction Using Adaptive Neuro-Fuzzy Inference System

Seizure Prediction Using Adaptive Neuro-Fuzzy Inference Syst...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Ahmed F. Rabbi Leila Azinfar Reza Fazel-Rezai Biomedical Signal and Image Processing Laboratory Electrical Engineering Department The

ISBN: (纸本)9781457702150

In this study, we present a neuro-fuzzy approach of seizure prediction from invasive Electroencephalogram (EEG) by applying adaptive neuro-fuzzy inference system (ANFIS). Three nonlinear seizure predictive features were extracted from a patient's data obtained from the European Epilepsy Database, one of the most comprehensive EEG database for epilepsy research. A total of 36 hours of recordings including 7 seizures was used for analysis. The nonlinear features used in this study were similarity index, phase synchronization, and nonlinear interdependence. We designed an ANFIS classifier constructed based on these features as input. Fuzzy if-then rules were generated by the ANFIS classifier using the complex relationship of feature space provided during training. The membership function optimization was conducted based on a hybrid learning algorithm. The proposed method achieved highest sensitivity of 80% with false prediction rate as low as 0.46 per hour.

关键词： Seizures Reasoning System Epilepsy adaptive neuro fuzzy inference system Eigenvector fuzzy method fuzzy inference system Self tuning Classifiers nonlinearity Recordings

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Spectral clustering approach with sparsifying technique for functional connectivity detection in the resting brain

Spectral clustering approach with sparsifying technique for ...

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2010 International Conference on Intelligent and Advanced Systems, ICIAS 2010

作者： Ramezani, Mahdi Heidari, Amin Fatemizadeh, Emad Soltanian-Zadeh, Hamid Biomedical Signal and Image Processing Laboratory Sharif University of Technology Tehran Iran Radiology Image Analysis Laboratory Henry Ford Health System Detroit MI 48202 United States Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department University of Tehran Iran

ISBN: (纸本)9781424466238

The aim of this study is to assess the functional connectivity from resting state functional magnetic resonance imaging (fMRI) data. Spectral clustering algorithm was applied to the realistic and real fMRI data acquired from a resting healthy subject to find functionally connected brain regions. In order to make computation of the spectral decompositions of the entire brain volume feasible, the similarity matrix has been sparsified with the t-nearestneighbor approach. Realistic data were created to investigate the performance of the proposed algorithm and comparing it to the recently proposed spectral clustering algorithm with the Nystrom approximation and also with some well-known algorithms such as the Cross Correlation Analysis (CCA) and the spatial Independent Component Analysis (sICA). To enhance the performance of the methods, a variety of data pre and post processing steps, including data normalization, outlier removal, dimensionality reduction by using wavelet coefficients, estimation of number of clusters and optimal number of independent components (ICs). Results demonstrate the applicability of the proposed algorithm for functional connectivity analysis.

关键词： Brain

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Development of an Intelligent System for Detection of Chronic Stress from Biological signal processing 9th

Development of an Intelligent System for Detection of Chroni...

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9th Latin American Congress on biomedical Engineering, CLAIB 2022 and 28th Brazilian Congress on biomedical Engineering, CBEB 2022

作者： Junqueira, Luis Pina, Marta Biomedical Engineering Laboratory of Signal Processing and Medical Image SP São Paulo Brazil Federal Institute of Education Science and Technology IFSP SP São Paulo Brazil

ISBN: (纸本)9783031494031

Our objective in this work was to develop an intelligent system able to perform an automated detection of chronic stress, based on biological signals processing and features extraction, confronted with Hans Selye clinical model of stress phase’s assessment. We recorded biological signals of blood pressure, skin surface temperature, galvanic skin resistance and heart rate of 120 health adult volunteers. Also the heart rate variability parameters were extracted for the time-domain and frequency-domain analysis. A Multi-Layer Perceptron Artificial Neural Network with a supervised learning approach was applied to generate the mathematical model. The system classification produced a precision index of 89% for identifying the stressed class, when applying the skin temperature and the heart rate variability parameters as input of the neural network, providing a satisfactory initial performance in the discrimination of stressed individuals. A worth training set with more examples mighty potentially increases the precision for identifying the stressed individuals. Despite study limitations, we consider that the use of intelligent systems to classify biological signals and identify the long term stress presence in human organism could contribute to a more objective analysis of such physiologic characteristics associated to the chronic stress and its implications in human health and performance. Future works should include different settle of physiological parameters and different machine learning techniques for analysis and classification of signals. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Intelligent systems

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Robust Sleep Apnea Monitoring using Heart Rate Variability and Extended Kalman Classification based on Single Lead ECG

Robust Sleep Apnea Monitoring using Heart Rate Variability a...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Vahid Asadpour Reza Fazel-Rezai Elahe Alibakhshian Biomedical Signal and Image Processing Laboratory Electrical Engineering Department The University of North Dakota Biomedical Signal and Image Processing Laboratory Electrical Engineering Department University of North Dakota Azad University

ISBN: (纸本)9781457702150

Sleep apnea diagnosis requires analysis of long term polysomnographic signal during one period of night sleep. Limited access to sleep laboratories, various required devices and dedicated assistants made the diagnosis of sleep apnea underestimated and not easily accessible to the general population. In this work, a classification method based on modified Kalman filter which uses heart rate variability (HRV) wavelet signal obtained from a single electrocardiogram (ECG) lead is proposed. A pre-filtering was performed on wavelet transform to improve the correlation of extracted features. Sample entropy was used to enhance the convergence rate and accuracy of classification. The performance of the proposed method was evaluated in terms of accuracy, sensitivity and specificity. The classifier overcomes these methods by 53% to 7.2% improvements in accuracy.

关键词： Sleep Apnea Syndromes heart rate variability ecg lead apnea monitoring Classification Methods night sleep Kalman filters wavelets

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Development of Alzheimer's Disease Recognition using Semiautomatic Analysis of Statistical Parameters based on Frequency Characteristics of Medical images

Development of Alzheimer's Disease Recognition using Semiaut...

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IEEE International Conference on signal processing and Communications, ICSPC

作者： Meysam Torabi Hassan Moradzadeh Reza Vaziri S. Mohammad Javad Razavian Reza Dehestani Ardekani Moones Rahmandoust Ali Taalimi Emad Fatemizadeh Sharif University of Technology Iran Iran University of Science and Technology Iran Biomedical Signal and Image Processing Laboratory Sharif University of Technology Iran Biomedical Signal and Image Processing Laboratory Kashan University Iran

The paper presents an effective algorithm to analyze MR-images in order to recognize Alzheimer's disease (AD) which appeared in patient's brain. The features of interest are categorized in features of the spatial domain (FSD's) and Features of the frequency domain (FFD's) which are based on the first four statistic moments of the wavelet transform. Extracted features have been classified by a multi-layer perceptron artificial neural network (ANN). Before ANN, the number of features is reduced from 44 to 12 to optimize and eliminate any correlation between them. The contribution of this paper is to demonstrate that by using the wavelet transform number of features needed for AD diagnosis has been reduced in comparison with the previous work. We achieved 79% and 100% accuracy among test set and training set respectively, including 93 MR-images.

关键词： Alzheimer's disease Character recognition image recognition image analysis Frequency biomedical imaging Artificial neural networks Wavelet transforms Medical diagnostic imaging Algorithm design and analysis

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Seizure Detection in EEG signals: A Comparison of Different approaches

Seizure Detection in EEG signals: A Comparison of Different ...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Hamid R. Mohseni A. Maghsoudi Mohammad B. Shamsollahi Biomedical Signal and Image Processing Laboratory BiSIPL Sharif University of Technology Tehran Iran

In this paper, the performance of traditional variance-based method for detection of epileptic seizures in EEG signals are compared with various methods based on nonlinear time series analysis, entropies, logistic regression, discrete wavelet transform and time frequency distributions. We noted that variance-based method in compare to the mentioned methods had the best result (100%) applied on the same database.

关键词： Electroencephalography signal detection Epilepsy signal analysis Wavelet analysis Discrete wavelet transforms Frequency Fourier transforms Logistics Performance analysis

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A robust FCM algorithm for image segmentation based on spatial information and Total Variation

A robust FCM algorithm for image segmentation based on spati...

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Iranian Machine Vision and image processing (MVIP)

作者： Hassan Akbari Hamed Mohebbi Kalkhoran Emad Fatemizadeh Biomedical Signal and Image Processing Laboratory (BiSIPL) Sharif University of Technology Tehran Iran

image segmentation with clustering approach is widely used in biomedical application. Fuzzy c-means (FCM) clustering is able to preserve the information between tissues in image, but not taking spatial information into account, makes segmentation results of the standard FCM sensitive to noise. To overcome the above shortcoming, a modified FCM algorithm for MRI brain image segmentation is presented in this paper. The algorithm is realized by incorporating the spatial neighborhood information into the standard FCM algorithm and modifying the membership weighting of each cluster by smoothing it by Total Variation (TV) denoising. The proposed algorithm is evaluated with accuracy index in performing it on artificial synthesized images, and the results show the superior accuracy compared to some other state of the art FCM-based segmentation methods.

关键词： image segmentation biomedical imaging Magnetic resonance imaging Clustering algorithms Artificial intelligence

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Robot Control Using an Inexpensive P300 Based BCI

Robot Control Using an Inexpensive P300 Based BCI

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Iranian Conference of biomedical Engineering (ICBME)

作者： Shafie Bahman Mohammad B. Shamsollahi Biomedical Signal and Image Processing Laboratory (BiSIPL) Sharif University of Technology Tehran Iran

ISBN: (数字)9781728156637

ISBN: (纸本)9781728156644

Brain Computer Interfaces (BCI) are an important concept of biomedical engineering because of their ability to improve life conditions for people with different disabilities. Lots of studies have worked on important characteristics of BCI systems, such as speed and accuracy, whereas price is an important aspect too. Thus, a low cost BCI system with high accuracy clearly can help more people. Our purpose in this study is to design a P300 based BCI system using a low-priced EEG headset which has an acceptable accuracy. Our final design got a mean real-time accuracy of 93.3% which is comparable to systems with much more expensive hardware.

关键词： Electroencephalography Robots Real-time systems Task analysis Graphical user interfaces Mathematical model biomedical engineering

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