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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 12/2019

28-01-2019 | Original Article

A novel EEG-complexity-based feature and its application on the epileptic seizure detection

Authors: Shu-Ling Zhang, Bo Zhang, Yong-Li Su, Jiang-Ling Song

Published in: International Journal of Machine Learning and Cybernetics | Issue 12/2019

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Abstract

The neurophysiology system is a complex network of nerves and cells, which carries messages to and from the brain and spinal cord to various parts of the body. Exploring complexity of the system can be contributed to understand diverse neurophysiological abnormalities, which may further result in different kinds of neurological disorders. In this paper, we present a novel analyzing framework to characterize the complexity of neurophysiological system, under which a specific weighted FPE-complexity-based feature (W-FPE-F) is extracted from EEG and then applied into the automated epileptic seizure detection. Combining with extreme learning machine (ELM) and support vector machine (SVM), performances of the proposed method are finally verified on two open EEG databases. Simulation results show that the proposed method does a good job in detecting the epileptic seizure, particularly, it is able to avoid the undesirable detection performance caused by individual divergence effectively.
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Metadata
Title
A novel EEG-complexity-based feature and its application on the epileptic seizure detection
Authors
Shu-Ling Zhang
Bo Zhang
Yong-Li Su
Jiang-Ling Song
Publication date
28-01-2019
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 12/2019
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-019-00921-w

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