Feature selection and classifier parameters estimation for EEG signals peak detection using particle swarm optimization

Asrul Adam, Mohd Ibrahim Shapiai, Mohd Zaidi Mohd Tumari, Mohd Saberi Mohamad, Marizan Mubin

Research output: Contribution to journalArticlepeer-review

33 Citations (Scopus)

Abstract

Electroencephalogram (EEG) signal peak detection is widely used in clinical applications. The peak point can be detected using several approaches, including time, frequency, time-frequency, and nonlinear domains depending on various peak features from several models. However, there is no study that provides the importance of every peak feature in contributing to a good and generalized model. In this study, feature selection and classifier parameters estimation based on particle swarm optimization (PSO) are proposed as a framework for peak detection on EEG signals in time domain analysis. Two versions of PSO are used in the study: (1) standard PSO and (2) random asynchronous particle swarm optimization (RA-PSO). The proposed framework tries to find the best combination of all the available features that offers good peak detection and a high classification rate from the results in the conducted experiments. The evaluation results indicate that the accuracy of the peak detection can be improved up to 99.90% and 98.59% for training and testing, respectively, as compared to the framework without feature selection adaptation. Additionally, the proposed framework based on RA-PSO offers a better and reliable classification rate as compared to standard PSO as it produces low variance model.

Original languageEnglish
Article number973063
JournalScientific World Journal
Volume2014
DOIs
Publication statusPublished - 2014
Externally publishedYes

ASJC Scopus subject areas

  • General Biochemistry,Genetics and Molecular Biology
  • General Environmental Science

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