Universiti Teknologi Malaysia Institutional Repository

Dingle's model-based EEG peak detection using a rule-based classifier

Adam, Asrul and Mokhtar, Norrima and Mubin, Marizan and Ibrahim, Zuwairie and Shapiai @ Abd. Razak, Mohd. Ibrahim (2015) Dingle's model-based EEG peak detection using a rule-based classifier. In: The International Conference on Artificial Life and Robotics 2015 (ICAROB 2015) 20th Arob Anniversary, 10-12 Jan, 2015, Japan.

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Official URL: http://alife-robotics.co.jp/Call%20for%20Papers.pd...

Abstract

The employment of peak detection algorithm is prominent in several clinical applications such as diagnosis and treatment of epilepsy patients, assisting to determine patient syndrome, and guiding paralyzed patients to manage some devices. In this study, the performances of four different peak models of time domain approach which are Dumpala's, Acir's, Liu's, and Dingle's peak models are evaluated for electroencephalogram (EEG) signal peak detection algorithm. The algorithm is developed into three stages: peak candidate detection, feature extraction, and classification. Rule-based classifier with an estimation technique based on particle swarm optimization (PSO) is employed in the classification stage. The evaluation result shows that the best peak model is Dingle's peak model with the highest test performance is 88.78%.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:electroencephalogram (EEG) signal, peak detection
Subjects:T Technology > T Technology (General)
Divisions:Malaysia-Japan International Institute of Technology
ID Code:61186
Deposited By: Widya Wahid
Deposited On:19 Mar 2017 06:58
Last Modified:21 Aug 2017 04:12

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