title: Evaluation of different time domain peak models using extreme learning machine-based peak detection for EEG signal.
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.
authors.affiliation: Applied Control and Robotics (ACR) Laboratory, Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia.
authors.forename: Asrul
authors.initials: A
authors.lastname: Adam
authors.affiliation: Faculty of Electrical and Electronic Engineering, Universiti Malaysia Pahang, 26600 Pekan, Pahang Malaysia.
authors.forename: Zuwairie
authors.initials: Z
authors.lastname: Ibrahim
authors.affiliation: Applied Control and Robotics (ACR) Laboratory, Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia.
authors.forename: Norrima
authors.initials: N
authors.lastname: Mokhtar
authors.affiliation: Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia Kuala Lumpur, Jalan Semarak, 54100 Kuala Lumpur, Malaysia.
authors.forename: Mohd Ibrahim
authors.initials: MI
authors.lastname: Shapiai
authors.affiliation: School of Psychology and Counseling, Queensland University of Technology, and QIMR Berghofer, Brisbane, Australia.
authors.forename: Paul
authors.initials: P
authors.lastname: Cumming
authors.affiliation: Applied Control and Robotics (ACR) Laboratory, Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia.
authors.forename: Marizan
authors.initials: M
authors.lastname: Mubin
keywords: Electroencephalogram (EEG); Extreme learning machines (ELM); Pattern recognition; Peak detection algorithm; Peak model