Machine Learning Classification of Epileptic Seizures Based on EEG | using Third-Ordered Cumulants and Adaptive Fractal Analysis Techniques

Buchanna Gajula

ISBN 10: 6206152960 / ISBN 13: 9786206152965

Published by LAP LAMBERT Academic Publishing, 2023

About this Item

Machine Learning Classification of Epileptic Seizures Based on EEG | using Third-Ordered Cumulants and Adaptive Fractal Analysis Techniques | Buchanna Gajula | Taschenbuch | Englisch | 2023 | LAP LAMBERT Academic Publishing | EAN 9786206152965 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Seller Inventory # 126866303

Bibliographic Details

• Title: Machine Learning Classification of Epileptic Seizures Based on EEG | using Third-Ordered Cumulants and Adaptive Fractal Analysis Techniques
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2023
• Language: English
• ISBN 10: 6206152960
• ISBN 13: 9786206152965
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 9D millimeters 220 millimeters width by 150 millimeters height by 9 millimeters depth
• Weight: 209 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

Machine Learning Classification of Epileptic Seizures Based on Electroencephalogram using Third-Ordered Cumulants and Adaptive Fractal Analysis Techniques1 IntroductionEpilepsy is one of the serious neurological diseases in the world. Indeed, early detection of epileptic seizures will extend the life span of epileptic patients. In this regard, a lot of efforts has been done to predict epileptic seizures based on electroencephalography (EEG) signals. In literature, there are many feature-based seizure classification methods quoted. No method is proved perfectly in capturing a standard set of features with the dynamics of signals. It is a common neurological disorder caused by the abnormally rapid release of brain nerve cells that is characterized by seizures. The scalp or intracranial Electroencephalogram (EEG) signals obtained in the clinic typically exhibit characteristics such as chaos, nonlinearity, etc.Deep neural networks have made significant advances in the field of machine learning during the last era. The model can learn effective representations from raw data in both supervised and unsupervised contexts by creating a hierarchical or "deep" structure.