ISBN: 9780128174265 - eeg brain signal classification for epileptic seizure disorder detection (10 Ergebnisse)

Zustand: Used. pp. 150.

Zustand: Used. pp. 150.

Zustand: New.

Zustand: As New. Unread book in perfect condition.

Zustand: New. In English.

Zustand: As New. Unread book in perfect condition.

Taschenbuch. Zustand: Neu. EEG Brain Signal Classification for Epileptic Seizure Disorder Detection | Sandeep Kumar Satapathy (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2019 | Elsevier Science & Technology | EAN 9780128174265 | Verantwortliche Person für die EU: Elsevier B.V., Radarweg 29, 1043 NX AMSTERDAM, NIEDERLANDE, productsafety[at]elsevier[dot]com | Anbieter: preigu. …

Zustand: New. 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, .

Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. Englisch. …

Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.…