eeg brain signal classification for epileptic seizure disorder detection

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EEG Brain Signal Classification for Epileptic Seizure Disorder Detection
Author : Sandeep Kumar Satapathy,Satchidananda Dehuri,Alok Kumar Jagadev,Shruti Mishra
Publisher : Academic Press
Release Date : 2019-02-10
ISBN 10 : 0128174277
Pages : 134 pages
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EEG Brain Signal Classification for Epileptic Seizure Disorder Detection provides the knowledge necessary to classify EEG brain signals to detect epileptic seizures using machine learning techniques. Chapters present an overview of machine learning techniques and the tools available, discuss previous studies, present empirical studies on the performance of the NN and SVM classifiers, discuss RBF neural networks trained with an improved PSO algorithm for epilepsy identification, and cover ABC algorithm optimized RBFNN for classification of EEG signal. Final chapter present future developments in the field. This book is a valuable source for bioinformaticians, medical doctors and other members of the biomedical field who need the most recent and promising automated techniques for EEG classification. Explores machine learning techniques that have been modified and validated for the purpose of EEG signal classification using Discrete Wavelet Transform for the identification of epileptic seizures Encompasses machine learning techniques, providing an easily understood resource for both non-specialized readers and biomedical researchers Provides a number of experimental analyses, with their results discussed and appropriately validated

EEG Brain Signal Classification for Epileptic Seizure Disorder Detection
Author : Sandeep Kumar Satapathy, PhD,Satchidananda Dehuri, PhD,Alok Kumar Jagadev, PhD,Shruti Mishra, PhD
Publisher : Academic Press
Release Date : 2019-02-21
ISBN 10 : 9780128174265
Pages : 150 pages
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EEG Brain Signal Classification for Epileptic Seizure Disorder Detection provides the knowledge necessary to classify EEG brain signals to detect epileptic seizures using machine learning techniques. Chapters present an overview of machine learning techniques and the tools available, discuss previous studies, present empirical studies on the performance of the NN and SVM classifiers, discuss RBF neural networks trained with an improved PSO algorithm for epilepsy identification, and cover ABC algorithm optimized RBFNN for classification of EEG signal. Final chapter present future developments in the field. This book is a valuable source for bioinformaticians, medical doctors and other members of the biomedical field who need the most recent and promising automated techniques for EEG classification. Explores machine learning techniques that have been modified and validated for the purpose of EEG signal classification using Discrete Wavelet Transform for the identification of epileptic seizures Encompasses machine learning techniques, providing an easily understood resource for both non-specialized readers and biomedical researchers Provides a number of experimental analyses, with their results discussed and appropriately validated

Brain Seizure Detection and Classification Using Electroencephalographic Signals
Author : Varsha K. Harpale,Vinayak Bairagi
Publisher : Academic Press
Release Date : 2021-07-15
ISBN 10 : 9780323911207
Pages : 232 pages
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Electroencephalogram (EEG) remains the most immediate, simple, and rich source of information for understanding phenomena related to brain electrical activities. The objective of the book is to analyze the EEG signals to observe abnormalities of brain activities called epileptic seizure. Seizure is a neurological disorder in which too many neurons are excited at the same time and are triggered by brain injury or by chemical imbalance. The seizures are predominantly characterized by unpredictable interruptions of normal brain function. A seizure occurs when too many nerve cells in the brain "fire” too quickly causing an "electrical storm.” The EEG signals recorded from epileptic patients are analyzed for monitoring extracting behavior of signals during onset seizures. Epileptic seizure detection still poses challenges in the field of accurate seizure detection and prediction of seizures. Mostly these techniques are analyzed on the basis of detection and classification accuracy, sensitivity and specificity. Brain Seizure Detection and Classification Using Electroencephalographic Signals presents EEG signal processing and analysis with high performance feature extraction. The Time and Frequency Domain (TFD), Wavelet Transform (WT) and Empirical Mode Decomposition (EMD) are optimized feature extraction methods presented by the authors. The book also covers the feature selection method based on One-way ANOVA along with high performance machine learning classifiers for classification of EEG signals in normal and epileptic EEG signals. In addition, the authors also present new methods of feature extraction, including Singular Spectrum-Empirical Wavelet Transform (SSEWT) for improved classification of seizures in significant seizure-types, specifically epileptic and Non-Epileptic Seizures (NES). The performance of the system will be compared with existing methods of feature extraction using Wavelet Transform (WT) and Empirical Wavelet Transform (EWT). The machine learning classifiers are used for classification of EEG signal in normal, epileptic seizure, non-epileptic seizure, and thus non-epileptic patients. One of the major new contributions of the book is identification of non-epileptic patients using SSEWT. Presents EEG signal processing and analysis with high performance feature extraction Discusses recent trends in seizure detection, prediction and classification methodologies Classification of epileptic and non-epileptic seizures is still a demanding issue, and misdiagnosing NES leads to the unnecessary use of antiepileptic medication, which can worsen NES and affect learning or working ability The authors present new guidance and technical discussion in these areas Presents a variety of feature-extraction methods, including Time and Frequency Domain (TFD), Wavelet Transform (WT), Empirical Mode Decomposition (EMD), and feature selection methods based on One-way ANOVA along with high performance machine learning classifiers for classification of EEG signals in normal and epileptic EEG signals. Presents new methods of feature extraction developed by the authors, including Singular Spectrum-Empirical Wavelet Transform (SSEWT) for improved classification of seizures in significant seizure-types, specifically epileptic and NonEpileptic Seizures (NES)

EEG Signal Analysis and Classification
Author : Siuly Siuly,Yan Li,Yanchun Zhang
Publisher : Springer
Release Date : 2017-01-03
ISBN 10 : 331947653X
Pages : 256 pages
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This book presents advanced methodologies in two areas related to electroencephalogram (EEG) signals: detection of epileptic seizures and identification of mental states in brain computer interface (BCI) systems. The proposed methods enable the extraction of this vital information from EEG signals in order to accurately detect abnormalities revealed by the EEG. New methods will relieve the time-consuming and error-prone practices that are currently in use. Common signal processing methodologies include wavelet transformation and Fourier transformation, but these methods are not capable of managing the size of EEG data. Addressing the issue, this book examines new EEG signal analysis approaches with a combination of statistical techniques (e.g. random sampling, optimum allocation) and machine learning methods. The developed methods provide better results than the existing methods. The book also offers applications of the developed methodologies that have been tested on several real-time benchmark databases. This book concludes with thoughts on the future of the field and anticipated research challenges. It gives new direction to the field of analysis and classification of EEG signals through these more efficient methodologies. Researchers and experts will benefit from its suggested improvements to the current computer-aided based diagnostic systems for the precise analysis and management of EEG signals. /div

2020 International Conference on Artificial Intelligence and Signal Processing (AISP)
Author : IEEE Staff
Publisher : N.A
Release Date : 2020-01-10
ISBN 10 : 9781728144597
Pages : 329 pages
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Artificial Intelligence implements computing technologies to make machines mimic several cognitive functions to provide solutions to specific problems These technologies address problems that involves reasoning, planning, knowledge representation, analysing, natural language processing, automation, robotics and perception, multiagent systems, applied statistical learning, and deep learning to develop newer applications every day In recent years, our everyday lives rely on the usage of computers, radios, cell phones which are enabled through signal processing It is the heart of today s technology without which sharing and information transfer becomes impossible Signal processing has very vast and interdisciplinary applications ranging from engineering field to medical sciences It has an immense role in providing exponential growth in the field of digital system, microchip, speech, image, and video processing, medical application, computer hardware, and also biosensor

Atlas of EEG, Seizure Semiology, and Management
Author : Karl E. Misulis
Publisher : Oxford University Press
Release Date : 2013-12
ISBN 10 : 0199985901
Pages : 400 pages
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Atlas of EEG, Seizure Semiology, & Management, Second Edition, is a richly-illustrated guide to the performance and interpretation of EEG and management of epilepsy. Revised and updated in its Second Edition, this new text features hundreds of detailed EEGs, and covers the science in extensive scope and detail, beginning with basic electronics and physiology and then moving through EEG interpretation, epilepsy diagnosis, and ultimately epilepsy management. The new edition also includes all basic classifications and definitions of seizures and epilepsy, making it the perfect clinical companion. Atlas of EEG, Seizure Semiology, & Management utilizes full-color EEG presentations, alongside an easy-to-read synthesis of anatomy, physiology, and available treatment modalities. These detailed explanations of wave pattern, presentatoin, and treatment provide the student and practitioner with the most informed sense of clinical application and readiness. Atlas of EEG, Seizure Semiology, & Management covers every type of seizure, both epileptic and non-epileptic and divided into eight concise chapters. This unique atlas is necessary reading for all practicing neurologists, fellows, and residents.

EEG Signal Processing
Author : Saeid Sanei,Jonathon A. Chambers
Publisher : John Wiley & Sons
Release Date : 2013-05-28
ISBN 10 : 1118691237
Pages : 312 pages
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Electroencephalograms (EEGs) are becoming increasingly important measurements of brain activity and they have great potential for the diagnosis and treatment of mental and brain diseases and abnormalities. With appropriate interpretation methods they are emerging as a key methodology to satisfy the increasing global demand for more affordable and effective clinical and healthcare services. Developing and understanding advanced signal processing techniques for the analysis of EEG signals is crucial in the area of biomedical research. This book focuses on these techniques, providing expansive coverage of algorithms and tools from the field of digital signal processing. It discusses their applications to medical data, using graphs and topographic images to show simulation results that assess the efficacy of the methods. Additionally, expect to find: explanations of the significance of EEG signal analysis and processing (with examples) and a useful theoretical and mathematical background for the analysis and processing of EEG signals; an exploration of normal and abnormal EEGs, neurological symptoms and diagnostic information, and representations of the EEGs; reviews of theoretical approaches in EEG modelling, such as restoration, enhancement, segmentation, and the removal of different internal and external artefacts from the EEG and ERP (event-related potential) signals; coverage of major abnormalities such as seizure, and mental illnesses such as dementia, schizophrenia, and Alzheimer’s disease, together with their mathematical interpretations from the EEG and ERP signals and sleep phenomenon; descriptions of nonlinear and adaptive digital signal processing techniques for abnormality detection, source localization and brain-computer interfacing using multi-channel EEG data with emphasis on non-invasive techniques, together with future topics for research in the area of EEG signal processing. The information within EEG Signal Processing has the potential to enhance the clinically-related information within EEG signals, thereby aiding physicians and ultimately providing more cost effective, efficient diagnostic tools. It will be beneficial to psychiatrists, neurophysiologists, engineers, and students or researchers in neurosciences. Undergraduate and postgraduate biomedical engineering students and postgraduate epileptology students will also find it a helpful reference.

Digital Health Approach for Predictive, Preventive, Personalised and Participatory Medicine
Author : Lotfi Chaari
Publisher : Springer
Release Date : 2019-07-10
ISBN 10 : 3030118002
Pages : 88 pages
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This collection, entitled Digital Health for Predictive, Preventive, Personalized and Participatory Medicine contains the proceedings of the first International conference on digital healthtechnologies (ICDHT 2018). Ten recent contributions in the fields of Artificial Intelligence (AI) and machine learning, Internet of Things (IoT) and data analysis, all applied to digital health. This collection enables researchers to learn about recent advances in the above mentioned fields. It brings a technological viewpoint of P4 medicine. Readers will discover how advanced Information Technology (IT) tools can be used for healthcare. For instance, the use of connected objects to monitor physiological parameters is discussed. Moreover, even if compressed sensing is nowadays a common acquisition technique, its use for IoT is presented in this collection through one of the pioneer works in the field. In addition, the use of AI for epileptic seizure detection is also discussed as being one of the major concerns of predictive medicine both in industrialized and low-income countries. This work is edited by Prof. Lotfi Chaari, professor at the University of Sfax, and previously at the University of Toulouse. This work comes after more than ten years of expertise in the biomedical signal and image processing field.

2019 International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST)
Author : IEEE Staff
Publisher : N.A
Release Date : 2019-01-10
ISBN 10 : 9781538680155
Pages : 329 pages
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Bioengineering,Communication, Circuits, Devices & Systems,Computing & Processing (Hardware Software), Engineering Profession, Electromagnetics, Photonics & Electro Optics,Power, Energy, Industry Applications,Robotics & Control Systems, Signal Processing & Analysis

EEG/ERP Analysis
Author : Kamel Nidal,Aamir Saeed Malik
Publisher : CRC Press
Release Date : 2014-10-23
ISBN 10 : 1482224712
Pages : 334 pages
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Changes in the neurological functions of the human brain are often a precursor to numerous degenerative diseases. Advanced EEG systems and other monitoring systems used in preventive diagnostic procedures incorporate innovative features for brain monitoring functions such as real-time automated signal processing techniques and sophisticated amplifiers. Highlighting the US, Europe, Australia, New Zealand, Japan, Korea, China, and many other areas, EEG/ERP Analysis: Methods and Applications examines how researchers from various disciplines have started to work in the field of brain science, and explains the different techniques used for processing EEG/ERP data. Engineers can learn more about the clinical applications, while clinicians and biomedical scientists can familiarize themselves with the technical aspects and theoretical approaches. This book explores the recent advances involved in EEG/ERP analysis for brain monitoring, details successful EEG and ERP applications, and presents the neurological aspects in a simplified way so that those with an engineering background can better design clinical instruments. It consists of 13 chapters and includes the advanced techniques used for signal enhancement, source localization, data fusion, classification, and quantitative EEG. In addition, some of the chapters are contributed by neurologists and neurosurgeons providing the clinical aspects of EEG/ERP analysis. Covers a wide range of EEG/ERP applications with state-of-the-art techniques for denoising, analysis, and classification Examines new applications related to 3D display devices Includes MATLAB® codes EEG/ERP Analysis: Methods and Applications is a resource for biomedical and neuroscience scientists who are working on neural signal processing and interpretation, and biomedical engineers who are working on EEG/ERP signal analysis methods and developing clinical instrumentation. It can also assist neurosurgeons, psychiatrists, and postgraduate students doing research in neural engineering, as well as electronic engineers in neural signal processing and instrumentation.

Seizure Prediction in Epilepsy
Author : Björn Schelter,Jens Timmer,Andreas Schulze-Bonhage
Publisher : John Wiley & Sons
Release Date : 2008-11-21
ISBN 10 : 3527625208
Pages : 369 pages
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Comprising some 30 contributions, experts from around the world present and discuss recent advances related to seizure prediction in epilepsy. The book covers an extraordinarily broad spectrum, starting from modeling epilepsy in single cells or networks of a few cells to precisely-tailored seizure prediction techniques as applied to human data. This unique overview of our current level of knowledge and future perspectives provides theoreticians as well as practitioners, newcomers and experts with an up-to-date survey of developments in this important field of research.

Neural Networks as Cybernetic Systems
Author : Holk Cruse
Publisher : George Thieme Verlag
Release Date : 1996-01-01
ISBN 10 : 9780865776722
Pages : 167 pages
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The "task" of a neural system is to guide an organism through a changing environment & to help it survive under varying conditions. The neuronal systems are probably the most complicated developed by nature. This book provides a combination of classical systems theory (dynamic systems with a small number of channels) & recent developments in the field of systems (massive parallel systems).