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

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...

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Main Authors: Mohd Zaidi, Mohd Tumari, Asrul, Adam, Mohd Ibrahim, Shapiai, Mohd Saberi, Mohamad, Marizan, Mubin
Format: Article
Language:English
Published: Hindawi Publishing Corporation 2014
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/6465/1/Feature_Selection_and_Classifier_Parameters_Estimation_for_EEG_Signals_Peak_Detection_Using_Particle_Swarm_Optimization.pdf
http://umpir.ump.edu.my/id/eprint/6465/
http://dx.doi.org/10.1155/2014/973063
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spelling my.ump.umpir.64652018-05-18T07:03:31Z http://umpir.ump.edu.my/id/eprint/6465/ Feature Selection and Classifier Parameters Estimation for EEG Signals Peak Detection Using Particle Swarm Optimization Mohd Zaidi, Mohd Tumari Asrul, Adam Mohd Ibrahim, Shapiai Mohd Saberi, Mohamad Marizan, Mubin TK Electrical engineering. Electronics Nuclear engineering 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. Hindawi Publishing Corporation 2014 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/6465/1/Feature_Selection_and_Classifier_Parameters_Estimation_for_EEG_Signals_Peak_Detection_Using_Particle_Swarm_Optimization.pdf Mohd Zaidi, Mohd Tumari and Asrul, Adam and Mohd Ibrahim, Shapiai and Mohd Saberi, Mohamad and Marizan, Mubin (2014) Feature Selection and Classifier Parameters Estimation for EEG Signals Peak Detection Using Particle Swarm Optimization. The Scientific World Journal, 2014. pp. 1-13. ISSN 2356-6140 (print); 1537-744X (online) http://dx.doi.org/10.1155/2014/973063 DOI: 10.1155/2014/973063
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Mohd Zaidi, Mohd Tumari
Asrul, Adam
Mohd Ibrahim, Shapiai
Mohd Saberi, Mohamad
Marizan, Mubin
Feature Selection and Classifier Parameters Estimation for EEG Signals Peak Detection Using Particle Swarm Optimization
description 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.
format Article
author Mohd Zaidi, Mohd Tumari
Asrul, Adam
Mohd Ibrahim, Shapiai
Mohd Saberi, Mohamad
Marizan, Mubin
author_facet Mohd Zaidi, Mohd Tumari
Asrul, Adam
Mohd Ibrahim, Shapiai
Mohd Saberi, Mohamad
Marizan, Mubin
author_sort Mohd Zaidi, Mohd Tumari
title Feature Selection and Classifier Parameters Estimation for EEG Signals Peak Detection Using Particle Swarm Optimization
title_short Feature Selection and Classifier Parameters Estimation for EEG Signals Peak Detection Using Particle Swarm Optimization
title_full Feature Selection and Classifier Parameters Estimation for EEG Signals Peak Detection Using Particle Swarm Optimization
title_fullStr Feature Selection and Classifier Parameters Estimation for EEG Signals Peak Detection Using Particle Swarm Optimization
title_full_unstemmed Feature Selection and Classifier Parameters Estimation for EEG Signals Peak Detection Using Particle Swarm Optimization
title_sort feature selection and classifier parameters estimation for eeg signals peak detection using particle swarm optimization
publisher Hindawi Publishing Corporation
publishDate 2014
url http://umpir.ump.edu.my/id/eprint/6465/1/Feature_Selection_and_Classifier_Parameters_Estimation_for_EEG_Signals_Peak_Detection_Using_Particle_Swarm_Optimization.pdf
http://umpir.ump.edu.my/id/eprint/6465/
http://dx.doi.org/10.1155/2014/973063
_version_ 1643665383631093760
score 13.211869

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