EEG Brain Signal Classification for Epileptic Seizure Disorder Detection Book

EEG Brain Signal Classification for Epileptic Seizure Disorder Detection


  • Author : Sandeep Kumar Satapathy
  • Publisher : Academic Press
  • Release Date : 2019-02-10
  • Genre: Medical
  • Pages : 134
  • ISBN 10 : 9780128174272

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EEG Brain Signal Classification for Epileptic Seizure Disorder Detection Excerpt :

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 EEG Signals Book

Brain Seizure Detection and Classification Using EEG Signals


  • Author : Varsha K. Harpale
  • Publisher : Academic Press
  • Release Date : 2021-09-09
  • Genre: Science
  • Pages : 176
  • ISBN 10 : 9780323911214

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Brain Seizure Detection and Classification Using EEG Signals Excerpt :

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). The performance of the system is compared with existing methods of feature extraction using Wavelet Transform (WT) and Empirical Wavelet Transform (EWT). The book's objective 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. Presents EEG signal processing and analysis concepts with high performance feature extraction Discusses recent trends in seizure detection, prediction and classification methodologies Helps classify epileptic and non-epileptic seizures where misdiagnosis may lead to the unnecessary use of antiepileptic medication Provides new guidance and technical discussions on feature-extraction methods 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, and new methods of feature extraction developed by the authors, including Singular Spectrum-Empirical Wavelet

Data Mining and Machine Learning Applications Book

Data Mining and Machine Learning Applications


  • Author : Rohit Raja
  • Publisher : John Wiley & Sons
  • Release Date : 2022-03-02
  • Genre: Computers
  • Pages : 496
  • ISBN 10 : 9781119791782

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Data Mining and Machine Learning Applications Excerpt :

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 analytics and machine learning involves the use of algorithms that automatically improve through experience based on data. Massive datasets can be classified and clustered to obtain accurate results. The most common technologies used include classification and clustering methods. Accuracy and error rates are calculated for regression and classification and clustering to find actual results through algorithms like support vector machines and neural networks with forward and backward propagation. Applications include fraud detection, image processing, medical diagnosis, weather prediction, e-commerce and so forth. The book features: A review of the state-of-the-art in data mining and machine learning, A review and description of the learning methods in human-computer interaction, Implementation strategies and future research directions used to meet the design and application requirements of several modern and real-time applications for a long time, The scope and implementation of a majority of data mining and machine learning strategies. A discussion of real-time problems. Audience Industry and academic researchers, scientists, and engineers in information technology, data science and machine and deep learning, as well as artificial intelligence more broadly.

EEG Signal Analysis and Classification Book

EEG Signal Analysis and Classification


  • Author : Siuly Siuly
  • Publisher : Springer
  • Release Date : 2017-01-03
  • Genre: Technology & Engineering
  • Pages : 256
  • ISBN 10 : 9783319476537

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EEG Signal Analysis and Classification Excerpt :

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

EEG Signal Processing Book
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From 1 Ratings

EEG Signal Processing


  • Author : Saeid Sanei
  • Publisher : John Wiley & Sons
  • Release Date : 2013-05-28
  • Genre: Science
  • Pages : 312
  • ISBN 10 : 9781118691236

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EEG Signal Processing Excerpt :

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 t

Early Detection of Neurological Disorders Using Machine Learning Systems Book

Early Detection of Neurological Disorders Using Machine Learning Systems


  • Author : Paul, Sudip
  • Publisher : IGI Global
  • Release Date : 2019-06-28
  • Genre: Medical
  • Pages : 376
  • ISBN 10 : 9781522585688

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Early Detection of Neurological Disorders Using Machine Learning Systems Excerpt :

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 book are artificial intelligence, data analysis, and biomedical informatics. It is designed for clinicians, doctors, neurologists, physiotherapists, neurorehabilitation specialists, scholars, academics, and students interested in topics centered on biomedical engineering, bio-electronics, medical electronics, physiology, neurosciences, life sciences, and physics.

KNN Classifier and K Means Clustering for Robust Classification of Epilepsy from EEG Signals  A Detailed Analysis Book

KNN Classifier and K Means Clustering for Robust Classification of Epilepsy from EEG Signals A Detailed Analysis


  • Author : Harikumar Rajaguru
  • Publisher : Anchor Academic Publishing
  • Release Date : 2017-05
  • Genre: Computers
  • Pages : 57
  • ISBN 10 : 9783960671404

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KNN Classifier and K Means Clustering for Robust Classification of Epilepsy from EEG Signals A Detailed Analysis Excerpt :

Epilepsy is a chronic disorder, the hallmark of which is recurrent, unprovoked seizures. Many people with epilepsy have more than one type of seizures and may have other symptoms of neurological problems as well. Epilepsy is caused due to sudden recurrent firing of the neurons in the brain. The symptoms are convulsions, dizziness and confusion. One out of every hundred persons experiences a seizure at some time in their lives. It may be confused with other events like strokes or migraines. Unfortunately, the occurrence of an epileptic seizure seems unpredictable and its process still is hardly understood. In India, the number of persons suffering from epilepsy is increasing every year. The complexity involved in the diagnosis and therapy has to be cost effective. In this project, the authors applied an algorithm which is used for a classification of the risk level of epilepsy in epileptic patients from Electroencephalogram (EEG) signals. Dimensionality reduction is done on the EEG dataset by applying Power Spectral density. The KNN Classifier and K-Means clustering is implemented on these spectral values to epilepsy risk level detection. The Performance Index (PI) and Quality Value (QV) are calculated for the above methods. A group of twenty patients with known epilepsy findings are used in this study.

Comprehensive Analysis of Extreme Learning Machine and Continuous Genetic Algorithm for Robust Classification of Epilepsy from EEG Signals Book

Comprehensive Analysis of Extreme Learning Machine and Continuous Genetic Algorithm for Robust Classification of Epilepsy from EEG Signals


  • Author : Harikumar Rajaguru
  • Publisher : Anchor Academic Publishing
  • Release Date : 2017-01
  • Genre: Computers
  • Pages : 36
  • ISBN 10 : 9783960670995

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Comprehensive Analysis of Extreme Learning Machine and Continuous Genetic Algorithm for Robust Classification of Epilepsy from EEG Signals Excerpt :

Epilepsy is a common and diverse set of chronic neurological disorders characterized by seizures. It is a paroxysmal behavioral spell generally caused by an excessive disorderly discharge of cortical nerve cells of the brain. Epilepsy is marked by the term “epileptic seizures”. Epileptic seizures result from abnormal, excessive or hyper-synchronous neuronal activity in the brain. About 50 million people worldwide have epilepsy, and nearly 80% of epilepsy occurs in developing countries. The most common way to interfere with epilepsy is to analyse the EEG (electroencephalogram) signal which is a non-invasive, multi channel recording of the brain’s electrical activity. It is also essential to classify the risk levels of epilepsy so that the diagnosis can be made easier. This study investigates the possibility of Extreme Learning Machine (ELM) and Continuous GA as a post classifier for detecting and classifying epilepsy of various risk levels from the EEG signals. Singular Value Decomposition (SVD), Principal Component Analysis (PCA) and Independent Component Analysis (ICA) are used for dimensionality reduction.

Epilepsy Book

Epilepsy


  • Author : Sandro Misciagna
  • Publisher : Unknown
  • Release Date : 2021
  • Genre: Epilepsy
  • Pages : null
  • ISBN 10 : 1839622903

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Epilepsy Excerpt :

Epilepsy Book

Epilepsy


  • Author : Dejan Stevanovic
  • Publisher : BoD – Books on Demand
  • Release Date : 2012-02-29
  • Genre: Science
  • Pages : 288
  • ISBN 10 : 9789535100829

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Epilepsy Excerpt :

With the vision of including authors from different parts of the world, different educational backgrounds, and offering open-access to their published work, InTech proudly presents the latest edited book in epilepsy research, Epilepsy: Histological, electroencephalographic, and psychological aspects. Here are twelve interesting and inspiring chapters dealing with basic molecular and cellular mechanisms underlying epileptic seizures, electroencephalographic findings, and neuropsychological, psychological, and psychiatric aspects of epileptic seizures, but non-epileptic as well.

Transfer and Multitask Learning Methods for Improving Brain Signal Analysis Book

Transfer and Multitask Learning Methods for Improving Brain Signal Analysis


  • Author : Boyu Wang
  • Publisher : Unknown
  • Release Date : 2017
  • Genre: Uncategoriezed
  • Pages : null
  • ISBN 10 : OCLC:979422411

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Transfer and Multitask Learning Methods for Improving Brain Signal Analysis Excerpt :

"The human brain is one of the most complicated biological systems in the world. The brain activities measured by various signals such as electroencephalogram (EEG), electrocorticogram (ECoG), and functional magnetic resonance imaging (fMRI) provide avenues that can help understand the underlying mechanisms of the brain as well as diagnosis brain disorders and the related diseases. However, without the proper techniques to analyze the brain signals, they are of limited value. In this thesis, we formulate the brain signal analysis as pattern recognition problems and emphasize the role of machine learning techniques in feature extraction and classification of EEG/ECoG signals, where we primarily consider two scenarios: epileptic seizure detection and translation of brain activities into control commands for a brain-computer interface (BCI) system. The first part of this thesis focuses on online cost-sensitive learning problem arise from epileptic seizure detection, where the data is collected incrementally over time, and the seizures are relatively rare compared to non-seizure brain activities. We generalize a number of batch cost-sensitive ensemble learning algorithms to the online setting, and show that the convergence of the proposed algorithms is guaranteed under certain conditions. In the second part of this thesis, we handle another learning paradigm called transfer and/or multitask learning in the context of online learning, which is also of practical value to develop effective patient-specific seizure detection algorithms. We follow the line of our work on online cost-sensitive ensemble learning, and present online boosting algorithms for transfer and multitask learning. The third contribution of this thesis consists of introducing a novel learning framework called the multitask generalized eigenvalue program, which is originally motivated by the spatial filter design for BCIs. By assuming that leading eigenvectors of related generalized eigenvalue problems (G

Seizure Detection with EEG Signals Using the Classification Learner Approach Book

Seizure Detection with EEG Signals Using the Classification Learner Approach


  • Author : You Long Wu
  • Publisher : Unknown
  • Release Date : 2018
  • Genre: Uncategoriezed
  • Pages : null
  • ISBN 10 : OCLC:1042256496

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Seizure Detection with EEG Signals Using the Classification Learner Approach Excerpt :

"Epilepsy is characterized by unpredictable seizures secondary to electrical abnormality in the brain. Electrical activity in the brain can be monitored by electroencephalogram (EEG). This is currently the most effective and convenient tool for seizure detection. A needed tool in this disease is a model that can detect disease processes. Classification is one of the most used supervised machine learning approaches. In order to train models that are able to "learn" how to classify new observations from examples of labeled input; this research focuses on evaluating the performance of multiple classifiers for seizure detection, by applying their corresponding prediction models to labeled inputs using MATLAB's classification learner application. Many types of classifiers are used in this research such as: decision trees, support vector machines, and logistic regression, amongst others. The result has demonstrated that bagged trees of the ensemble classifiers had the highest prediction accuracy among all classifiers, which could be helpful to other researchers who wish to investigate seizure detection from EEG signals using classification methods. Potentially this could be a useful clinical tool in the future." --

Intelligent Computing   Optimization Book

Intelligent Computing Optimization


  • Author : Ivan Zelinka
  • Publisher : Springer Nature
  • Release Date : 2022
  • Genre: Computational intelligence
  • Pages : 1332
  • ISBN 10 : 9783030932473

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Intelligent Computing Optimization Excerpt :

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. Objective was to celebrate Compassion and Wisdom with researchers, scholars, experts and investigators in Intelligent Computing and Optimization worldwide, to share knowledge, experience, innovationmarvelous opportunity for discourse and mutuality by novel research, invention and creativity. This proceedings book of ICO2021 is published by Springer NatureQuality Label of Excellence. .

Brain Informatics Book

Brain Informatics


  • Author : Peipeng Liang
  • Publisher : Springer Nature
  • Release Date : 2019-12-05
  • Genre: Computers
  • Pages : 274
  • ISBN 10 : 9783030370787

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Brain Informatics Excerpt :

This book constitutes the refereed proceedings of the 12th International Conference on Brain Informatics, BI 2019, held in Haikou, China, in December 2019. The 26 revised full papers were carefully reviewed and selected from 36 submissions. The papers are organized in the following topical sections: cognitive and computational foundations of brain science; human information processing systems; brain big data analysis, curation and management; informatics paradigms for brain and mental health research; and brain-machine intelligence and brain-inspired computing. Also included is a special session on computational social analysis for mental health.

Handbook of Research on Advancements of Artificial Intelligence in Healthcare Engineering Book
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Handbook of Research on Advancements of Artificial Intelligence in Healthcare Engineering


  • Author : Sisodia, Dilip Singh
  • Publisher : IGI Global
  • Release Date : 2020-02-28
  • Genre: Medical
  • Pages : 420
  • ISBN 10 : 9781799821229

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Handbook of Research on Advancements of Artificial Intelligence in Healthcare Engineering Excerpt :

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 provides comprehensive research on the possible applications of machine learning, deep learning, soft computing, and evolutionary computing techniques in the design, implementation, and optimization of healthcare engineering solutions. Featuring a wide range of topics such as genetic algorithms, mobile robotics, and neuroinformatics, this book is ideal for engineers, technology developers, IT consultants, hospital administrators, academicians, healthcare professionals, practitioners, researchers, and students.