Evaluation of Different Time Domain Peak Models using Extreme Learning Machine‐Based Peak Detection for EEG Signal

Asrul, Adam and Zuwairie, Ibrahim and Norrima, Mokhtar and Mohd Ibrahim, Shapiai and Cumming, Paul and Marizan, Mubin (2016) Evaluation of Different Time Domain Peak Models using Extreme Learning Machine‐Based Peak Detection for EEG Signal. SpringerPlus, 5 (1036). pp. 1-14. ISSN 2193-1801. (Published)

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Abstract

Various peak models have been introduced to detect and analyze peaks in the time domain analysis of electroencephalogram (EEG) signals. In general, peak model in the time domain analysis consists of a set of signal parameters, such as amplitude, width, and slope. Models including those proposed by Dumpala, Acir, Liu, and Dingle are routinely used to detect peaks in EEG signals acquired in clinical studies of epilepsy or eye blink. The optimal peak model is the most reliable peak detection performance in a particular application. A fair measure of performance of different models requires a common and unbiased platform. In this study, we evaluate the performance of the four different peak models using the extreme learning machine (ELM)-based peak detection algorithm. We found that the Dingle model gave the best performance, with 72 % accuracy in the analysis of real EEG data. Statistical analysis conferred that the Dingle model afforded significantly better mean testing accuracy than did the Acir and Liu models, which were in the range 37–52 %. Meanwhile, the Dingle model has no significant difference compared to Dumpala model.

Item Type:	Article
Uncontrolled Keywords:	Extreme learning machines (ELM), Electroencephalogram (EEG), Peak detection algorithm, Peak model, Pattern recognition
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division:	Faculty of Electrical & Electronic Engineering
Depositing User:	Prof. Madya Dr. Zuwairi Ibrahim
Date Deposited:	04 Aug 2016 03:00
Last Modified:	27 Apr 2018 07:56
URI:	http://umpir.ump.edu.my/id/eprint/13660
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