Full Description
This book presents an innovative method of EEG-based feature extraction and classification of seizures using EEG signals. It describes the methodology required for EEG analysis, seizure detection, seizure prediction, and seizure classification. It contains a compilation of techniques described in the literature and emphasizes newly proposed techniques. The book includes a brief discussion of existing methods for epileptic seizure diagnosis and prediction and introduces new efficient methods specifically for seizure prediction.
Focuses on the mathematical models and machine learning algorithms from a perspective of clinical deployment of EEG-based epileptic seizure prediction
Discusses recent trends in seizure detection, prediction, and classification methodologies
Provides engineering solutions to severity or risk analysis of detected seizures at remote places
Presents wearable solutions to seizure prediction
Includes details of the use of deep learning for epileptic seizure prediction using EEG
This book acts as a reference for academicians and professionals who are working in the field of computational biomedical engineering and are interested in the domain of EEG-based disease prediction.
Contents
1: Introduction 2: Electroencephalography 3: Epilepsy Detection 4: Existing Methods for Epileptic Seizure Prediction using Electroencphalegram Signals 5: Epileptic Seizure Prediction with EEG Signal Using Deep Recurrent Neural Network 6: Epileptic Seizure Prediction using Squirrle Atom Search Optimization Algorithm based Deep RNN 7: Contribution of Research and Conclusion
