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...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
|
| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/973063 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832562989702578176 |
|---|---|
| author | Asrul Adam Mohd Ibrahim Shapiai Mohd Zaidi Mohd Tumari Mohd Saberi Mohamad Marizan Mubin |
| author_facet | Asrul Adam Mohd Ibrahim Shapiai Mohd Zaidi Mohd Tumari Mohd Saberi Mohamad Marizan Mubin |
| author_sort | Asrul Adam |
| collection | DOAJ |
| 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 |
| id | doaj-art-fd836f9c0d81488e9fa89a02d430e6a8 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-fd836f9c0d81488e9fa89a02d430e6a82025-02-03T01:21:17ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/973063973063Feature Selection and Classifier Parameters Estimation for EEG Signals Peak Detection Using Particle Swarm OptimizationAsrul Adam0Mohd Ibrahim Shapiai1Mohd Zaidi Mohd Tumari2Mohd Saberi Mohamad3Marizan Mubin4Applied Control and Robotics (ACR) Laboratory, Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, MalaysiaFaculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, MalaysiaFaculty of Electrical and Electronic Engineering, Universiti Malaysia Pahang, 26600 Pekan, Pahang, MalaysiaFaculty of Computing, Universiti Teknologi Malaysia, 81310 Johor Bahru, MalaysiaApplied Control and Robotics (ACR) Laboratory, Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, MalaysiaElectroencephalogram (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.http://dx.doi.org/10.1155/2014/973063 |
| spellingShingle | Asrul Adam Mohd Ibrahim Shapiai Mohd Zaidi Mohd Tumari Mohd Saberi Mohamad Marizan Mubin Feature Selection and Classifier Parameters Estimation for EEG Signals Peak Detection Using Particle Swarm Optimization The Scientific World Journal |
| title | 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_short | 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 |
| url | http://dx.doi.org/10.1155/2014/973063 |
| work_keys_str_mv | AT asruladam featureselectionandclassifierparametersestimationforeegsignalspeakdetectionusingparticleswarmoptimization AT mohdibrahimshapiai featureselectionandclassifierparametersestimationforeegsignalspeakdetectionusingparticleswarmoptimization AT mohdzaidimohdtumari featureselectionandclassifierparametersestimationforeegsignalspeakdetectionusingparticleswarmoptimization AT mohdsaberimohamad featureselectionandclassifierparametersestimationforeegsignalspeakdetectionusingparticleswarmoptimization AT marizanmubin featureselectionandclassifierparametersestimationforeegsignalspeakdetectionusingparticleswarmoptimization |