Automatic Detection of Epilepsy and Seizure Using Multiclass Sparse Extreme Learning Machine Classification
Joint Authors
Wang, Yuanfa
Li, Zunchao
Feng, Lichen
Zheng, Chuang
Zhang, Wenhao
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-06-19
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
An automatic detection system for distinguishing normal, ictal, and interictal electroencephalogram (EEG) signals is of great help in clinical practice.
This paper presents a three-class classification system based on discrete wavelet transform (DWT) and the nonlinear sparse extreme learning machine (SELM) for epilepsy and epileptic seizure detection.
Three-level lifting DWT using Daubechies order 4 wavelet is introduced to decompose EEG signals into delta, theta, alpha, and beta subbands.
Considering classification accuracy and computational complexity, the maximum and standard deviation values of each subband are computed to create an eight-dimensional feature vector.
After comparing five multiclass SELM strategies, the one-against-one strategy with the highest accuracy is chosen for the three-class classification system.
The performance of the designed three-class classification system is tested with publicly available epilepsy dataset.
The results show that the system achieves high enough classification accuracy by combining the SELM and DWT and reduces training and testing time by decreasing computational complexity and feature dimension.
With excellent classification performance and low computation complexity, this three-class classification system can be utilized for practical epileptic EEG detection, and it offers great potentials for portable automatic epilepsy and seizure detection system in the future hardware implementation.
American Psychological Association (APA)
Wang, Yuanfa& Li, Zunchao& Feng, Lichen& Zheng, Chuang& Zhang, Wenhao. 2017. Automatic Detection of Epilepsy and Seizure Using Multiclass Sparse Extreme Learning Machine Classification. Computational and Mathematical Methods in Medicine،Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1142264
Modern Language Association (MLA)
Wang, Yuanfa…[et al.]. Automatic Detection of Epilepsy and Seizure Using Multiclass Sparse Extreme Learning Machine Classification. Computational and Mathematical Methods in Medicine No. 2017 (2017), pp.1-10.
https://search.emarefa.net/detail/BIM-1142264
American Medical Association (AMA)
Wang, Yuanfa& Li, Zunchao& Feng, Lichen& Zheng, Chuang& Zhang, Wenhao. Automatic Detection of Epilepsy and Seizure Using Multiclass Sparse Extreme Learning Machine Classification. Computational and Mathematical Methods in Medicine. 2017. Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1142264
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1142264