The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Epileptic Seizure Detection From EEG Signals Using Bagged Ensemble Approach
|
Author(s): Pradeep Singh (National Institute of Technology, Raipur, India)and Sujith Kumar Appikatla (National Institute of Technology, Raipur, India)
Copyright: 2020
Pages: 13
Source title:
Handbook of Research on Advancements of Artificial Intelligence in Healthcare Engineering
Source Author(s)/Editor(s): Dilip Singh Sisodia (National Institute of Technology, Raipur, India), Ram Bilas Pachori (Indian Institute of Technology, Indore, India)and Lalit Garg (University of Malta, Malta)
DOI: 10.4018/978-1-7998-2120-5.ch004
Purchase
|
Abstract
Seizures are caused by irregular electrical pulses in the brain. Epileptic seizure detection on EEG signals is a long process, which is done manually by epileptologists. The aim of the study is automatically detecting the seizures of the brain, given the electroencephalogram signals by feature extraction and processing through different machine learning algorithms. Machines can be trained to do this type of observation and predict the output with high accuracy. In this chapter, the classification study of individual and ensemble classifier is performed for epileptic seizure detection. The proposed method consists of two phases: extraction of data from EEG signals and development of an individual and ensemble models. Bagging ensemble is developed to achieve better results. The development of the ensemble using various classification algorithms contributes towards increasing the diversity of the ensemble. An extensive comparative study with existing benchmark algorithm is performed for epileptic seizure detection.
Related Content
Kamel Mouloudj, Vu Lan Oanh LE, Achouak Bouarar, Ahmed Chemseddine Bouarar, Dachel Martínez Asanza, Mayuri Srivastava.
© 2024.
20 pages.
|
José Eduardo Aleixo, José Luís Reis, Sandrina Francisca Teixeira, Ana Pinto de Lima.
© 2024.
52 pages.
|
Jorge Figueiredo, Isabel Oliveira, Sérgio Silva, Margarida Pocinho, António Cardoso, Manuel Pereira.
© 2024.
24 pages.
|
Fatih Pinarbasi.
© 2024.
20 pages.
|
Stavros Kaperonis.
© 2024.
25 pages.
|
Thomas Rui Mendes, Ana Cristina Antunes.
© 2024.
24 pages.
|
Nuno Geada.
© 2024.
12 pages.
|
|
|