Automatic Detection of Epilepsy and Seizure Using Multiclass Sparse Extreme Learning Machine Classification
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...
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Online Access: | http://dx.doi.org/10.1155/2017/6849360 |
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doaj-e5fbfe409d434e3c8fd048434293c5bb2020-11-24T22:35:07ZengHindawi LimitedComputational and Mathematical Methods in Medicine1748-670X1748-67182017-01-01201710.1155/2017/68493606849360Automatic Detection of Epilepsy and Seizure Using Multiclass Sparse Extreme Learning Machine ClassificationYuanfa Wang0Zunchao Li1Lichen Feng2Chuang Zheng3Wenhao Zhang4School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaSchool of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaSchool of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaSchool of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaSchool of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaAn 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.http://dx.doi.org/10.1155/2017/6849360 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yuanfa Wang Zunchao Li Lichen Feng Chuang Zheng Wenhao Zhang |
spellingShingle |
Yuanfa Wang Zunchao Li Lichen Feng Chuang Zheng Wenhao Zhang Automatic Detection of Epilepsy and Seizure Using Multiclass Sparse Extreme Learning Machine Classification Computational and Mathematical Methods in Medicine |
author_facet |
Yuanfa Wang Zunchao Li Lichen Feng Chuang Zheng Wenhao Zhang |
author_sort |
Yuanfa Wang |
title |
Automatic Detection of Epilepsy and Seizure Using Multiclass Sparse Extreme Learning Machine Classification |
title_short |
Automatic Detection of Epilepsy and Seizure Using Multiclass Sparse Extreme Learning Machine Classification |
title_full |
Automatic Detection of Epilepsy and Seizure Using Multiclass Sparse Extreme Learning Machine Classification |
title_fullStr |
Automatic Detection of Epilepsy and Seizure Using Multiclass Sparse Extreme Learning Machine Classification |
title_full_unstemmed |
Automatic Detection of Epilepsy and Seizure Using Multiclass Sparse Extreme Learning Machine Classification |
title_sort |
automatic detection of epilepsy and seizure using multiclass sparse extreme learning machine classification |
publisher |
Hindawi Limited |
series |
Computational and Mathematical Methods in Medicine |
issn |
1748-670X 1748-6718 |
publishDate |
2017-01-01 |
description |
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. |
url |
http://dx.doi.org/10.1155/2017/6849360 |
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