Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine

Song, Yuedong, Crowcroft, Jon and Zhang, Jiaxiang ORCID: https://orcid.org/0000-0002-4758-0394 2012. Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine. Journal of Neuroscience Methods 210 (2) , pp. 132-146. 10.1016/j.jneumeth.2012.07.003

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Abstract

Epilepsy is one of the most common neurological disorders – approximately one in every 100 people worldwide are suffering from it. The electroencephalogram (EEG) is the most common source of information used to monitor, diagnose and manage neurological disorders related to epilepsy. Large amounts of data are produced by EEG monitoring devices, and analysis by visual inspection of long recordings of EEG in order to find traces of epilepsy is not routinely possible. Therefore, automated detection of epilepsy has been a goal of many researchers for a long time. This paper presents a novel method for automatic epileptic seizure detection. An optimized sample entropy (O-SampEn) algorithm is proposed and combined with extreme learning machine (ELM) to identify the EEG signals regarding the existence of seizure or not. To the knowledge of the authors, there exists no similar work in the literature. A public dataset was utilized for evaluating the proposed method. Results show that the proposed epilepsy detection approach achieves not only high detection accuracy but also a very fast computation speed, which demonstrates its huge potential for the real-time detection of epileptic seizures.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Psychology
Subjects:	B Philosophy. Psychology. Religion > BF Psychology
Publisher:	Elsevier
ISSN:	0165-0270
Date of Acceptance:	10 July 2012
Last Modified:	27 Oct 2022 10:10
URI:	https://orca.cardiff.ac.uk/id/eprint/69144

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