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
This compact book presents a clear and thorough introduction to the object-oriented paradigm using the C++ language. It introduces the readers to various C++ features that support object-oriented programming (OOP) concepts. In an easy-to-comprehend format, the text teaches how to start and compile a C++ program and discusses the use of C++ in OOP. The book covers the full range of object-oriented topics, from the fundamental features through classes, inheritance, polymorphism, template, exception handling and standard template library. KEY FEATURES • Includes several pictorial descriptions of the concepts to facilitate better understanding. • Offers numerous class-tested programs and examples to show the practical application of theory. • Provides a summary at the end of each chapter to help students in revising all key facts. The book is designed for use as a text by undergraduate students of engineering, undergraduate and postgraduate students of computer applications, and postgraduate students of management.
This compact book presents a clear and thorough introduction to the object-oriented paradigm using the C++ language. It introduces the readers to various C++ features that support object-oriented programming (OOP) concepts. In an easy-to-comprehend format, the text teaches how to start and compile a C++ program and discusses the use of C++ in OOP. The book covers the full range of object-oriented topics, from the fundamental features through classes, inheritance, polymorphism, template, exception handling and standard template library. KEY FEATURES • Includes several pictorial descriptions of the concepts to facilitate better understanding. • Offers numerous class-tested programs and examples to show the practical application of theory. • Provides a summary at the end of each chapter to help students in revising all key facts. The book is designed for use as a text by undergraduate students of engineering, undergraduate and postgraduate students of computer applications, and postgraduate students of management.
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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