Feature Selection and Classifier Parameters Estimation for EEG Signals Peak Detection Using Particle Swarm Optimization

Joint Authors

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

Source

The Scientific World Journal

Issue

Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-13, 13 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2014-08-19

Country of Publication

Egypt

No. of Pages

13

Main Subjects

Medicine
Information Technology and Computer Science

Abstract EN

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.

American Psychological Association (APA)

Adam, Asrul& Shapiai, Mohd Ibrahim& Mohd Tumari, Mohd Zaidi& Mohamad, Mohd Saberi& Mubin, Marizan. 2014. Feature Selection and Classifier Parameters Estimation for EEG Signals Peak Detection Using Particle Swarm Optimization. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-13.
https://search.emarefa.net/detail/BIM-1051819

Modern Language Association (MLA)

Adam, Asrul…[et al.]. Feature Selection and Classifier Parameters Estimation for EEG Signals Peak Detection Using Particle Swarm Optimization. The Scientific World Journal No. 2014 (2014), pp.1-13.
https://search.emarefa.net/detail/BIM-1051819

American Medical Association (AMA)

Adam, Asrul& Shapiai, Mohd Ibrahim& Mohd Tumari, Mohd Zaidi& Mohamad, Mohd Saberi& Mubin, Marizan. Feature Selection and Classifier Parameters Estimation for EEG Signals Peak Detection Using Particle Swarm Optimization. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-13.
https://search.emarefa.net/detail/BIM-1051819

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1051819