EEG Brain Signal Classification for Epileptic Seizure Disorder Detection

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

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  • Author : Sandeep Kumar Satapathy
  • Publisher : Academic Press
  • Pages : 134 pages
  • ISBN : 0128174277
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKEEG Brain Signal Classification for Epileptic Seizure Disorder Detection

EEG Brain Signal Classification for Epileptic Seizure Disorder Detection

EEG Brain Signal Classification for Epileptic Seizure Disorder Detection
  • Author : Sandeep Kumar Satapathy,Satchidananda Dehuri,Alok Kumar Jagadev,Shruti Mishra
  • Publisher : Academic Press
  • Release : 10 February 2019
GET THIS BOOKEEG Brain Signal Classification for Epileptic Seizure Disorder Detection

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

Brain Seizure Detection and Classification Using EEG Signals

Brain Seizure Detection and Classification Using EEG Signals
  • Author : Varsha K. Harpale,Vinayak Bairagi
  • Publisher : Academic Press
  • Release : 09 September 2021
GET THIS BOOKBrain Seizure Detection and Classification Using EEG Signals

Brain Seizure Detection and Classification Using Electroencephalographic Signals presents EEG signal processing and analysis with high performance feature extraction. The book covers the feature selection method based on One-way ANOVA, along with high performance machine learning classifiers for the 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).

EEG Signal Analysis and Classification

EEG Signal Analysis and Classification
  • Author : Siuly Siuly,Yan Li,Yanchun Zhang
  • Publisher : Springer
  • Release : 03 January 2017
GET THIS BOOKEEG Signal Analysis and Classification

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

Data Mining and Machine Learning Applications

Data Mining and Machine Learning Applications
  • Author : Rohit Raja,Kapil Kumar Nagwanshi,Sandeep Kumar,K. Ramya Laxmi
  • Publisher : Wiley-Scrivener
  • Release : 15 March 2022
GET THIS BOOKData Mining and Machine Learning Applications

DATA MINING AND MACHINE LEARNING APPLICATIONS The book elaborates in detail on the current needs of data mining and machine learning and promotes mutual understanding among research in different disciplines, thus facilitating research development and collaboration. Data, the latest currency of today’s world, is the new gold. In this new form of gold, the most beautiful jewels are data analytics and machine learning. Data mining and machine learning are considered interdisciplinary fields. Data mining is a subset of data

EEG Signal Processing

EEG Signal Processing
  • Author : Saeid Sanei,Jonathon A. Chambers
  • Publisher : John Wiley & Sons
  • Release : 28 May 2013
GET THIS BOOKEEG Signal Processing

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,

Early Detection of Neurological Disorders Using Machine Learning Systems

Early Detection of Neurological Disorders Using Machine Learning Systems
  • Author : Paul, Sudip,Bhattacharya, Pallab,Bit, Arindam
  • Publisher : IGI Global
  • Release : 28 June 2019
GET THIS BOOKEarly Detection of Neurological Disorders Using Machine Learning Systems

While doctors and physicians are more than capable of detecting diseases of the brain, the most agile human mind cannot compete with the processing power of modern technology. Utilizing algorithmic systems in healthcare in this way may provide a way to treat neurological diseases before they happen. Early Detection of Neurological Disorders Using Machine Learning Systems provides innovative insights into implementing smart systems to detect neurological diseases at a faster rate than by normal means. The topics included in this

Automated EEG-Based Diagnosis of Neurological Disorders

Automated EEG-Based Diagnosis of Neurological Disorders
  • Author : Hojjat Adeli,Samanwoy Ghosh-Dastidar
  • Publisher : CRC Press
  • Release : 09 February 2010
GET THIS BOOKAutomated EEG-Based Diagnosis of Neurological Disorders

Based on the authors’ groundbreaking research, Automated EEG-Based Diagnosis of Neurological Disorders: Inventing the Future of Neurology presents a research ideology, a novel multi-paradigm methodology, and advanced computational models for the automated EEG-based diagnosis of neurological disorders. It is based on the ingenious integration of three different computing technologies and problem-solving paradigms: neural networks, wavelets, and chaos theory. The book also includes three introductory chapters that familiarize readers with these three distinct paradigms. After extensive research and the discovery of

Epileptic Seizures and the EEG

Epileptic Seizures and the EEG
  • Author : Andrea Varsavsky,Iven Mareels,Mark Cook
  • Publisher : CRC Press
  • Release : 19 April 2016
GET THIS BOOKEpileptic Seizures and the EEG

A study of epilepsy from an engineering perspective, this volume begins by summarizing the physiology and the fundamental ideas behind the measurement, analysis and modeling of the epileptic brain. It introduces the EEG and provides an explanation of the type of brain activity likely to register in EEG measurements, offering an overview of how these EEG records are and have been analyzed in the past. The book focuses on the problem of seizure detection and surveys the physiologically based dynamic

Atlas of EEG, Seizure Semiology, and Management

Atlas of EEG, Seizure Semiology, and Management
  • Author : Karl E. Misulis
  • Publisher : Oxford University Press
  • Release : 01 December 2013
GET THIS BOOKAtlas of EEG, Seizure Semiology, and Management

This resource is an illustrated guide to the performance and interpretation of EEG and management of epilepsy. This second edition has been thoroughly revised and updated, and features hundreds of detailed EEGs covering the science in extensive scope and detail, beginning with basic electronics and physiology, followed by EEG interpretation, epilepsy diagnosis, and ultimately epilepsy management. It also includes all basic classifications and definitions of seizures and epilepsy.

Epilepsy in Women

Epilepsy in Women
  • Author : Cynthia Harden,Sanjeev V. Thomas,Torbjorn Tomson
  • Publisher : John Wiley & Sons
  • Release : 02 January 2013
GET THIS BOOKEpilepsy in Women

Epilepsy requires careful management and monitoring through a woman’s life Epilepsy is a complex disease. The hormonal changes women experience, both day-to-day menstrual fluctuations and the longer waxes and wanings of a reproductive lifetime, make the management of epilepsy even more complicated. At some point, the well-being of a second person, a fetus, might also have to be taken into account. Epilepsy in Women provides a detailed guide to this challenge. The wide-ranging approach encompasses all aspects of a

2019 International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST)

2019 International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST)
  • Author : IEEE Staff
  • Publisher : Unknown Publisher
  • Release : 10 January 2019
GET THIS BOOK2019 International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST)

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

Intelligent Computing & Optimization

Intelligent Computing & Optimization
  • Author : Ivan Zelinka
  • Publisher : Springer Nature
  • Release : 24 May 2022
GET THIS BOOKIntelligent Computing & Optimization

This book of Springer Nature is another proof of Springers outstanding and greatness on the lively interface of Smart Optimization, Computational Science, Human Intelligence and Machine Learning! It is a Master Piece of what our community of Academics and Experts can provide when an Interdisciplinary Approach of Joint, Mutual and Deep Learning is supported by Modern Mathematics and Experience of the World-Leader Springer Nature! Fourth edition of International Conference on Intelligent Computing and Optimization took place at December 3031, 2021, via ZOOM.

Handbook of Research on Advancements of Artificial Intelligence in Healthcare Engineering

Handbook of Research on Advancements of Artificial Intelligence in Healthcare Engineering
  • Author : Sisodia, Dilip Singh,Pachori, Ram Bilas,Garg, Lalit
  • Publisher : IGI Global
  • Release : 28 February 2020
GET THIS BOOKHandbook of Research on Advancements of Artificial Intelligence in Healthcare Engineering

Artificial intelligence (AI) is revolutionizing every aspect of human life including human healthcare and wellbeing management. Various types of intelligent healthcare engineering applications have been created that help to address patient healthcare and outcomes such as identifying diseases and gathering patient information. Advancements in AI applications in healthcare continue to be sought to aid rapid disease detection, health monitoring, and prescription drug tracking. TheHandbook of Research on Advancements of Artificial Intelligence in Healthcare Engineering is an essential scholarly publication that