doi: 10.4304/jsw.9.1.230-239
Optimal Classification of Epileptic EEG Signals Using Neural Networks and Harmony Search Methods
- 1 College of Information Engineering, Shanghai Maritime University, China
- 2 Department of Automation and Systems Technology, Aalto University School of Electrical Engineering, Finland
- 3 Laboratory of Image Processing and Pattern Recognition, Beijing Normal University, China
- 4 Department of Biomedical Engineering, Tampere University of Technology, Finland
- 5 School of Foundational Education, Peking University Health Science Center, China
Abstract
In this paper, the Harmony Search (HS)-aided BP neural networks are used for the classification of the epileptic electroencephalogram (EEG) signals. It is well known that the gradient descent-based learning method can result in local optima in the training of BP neural networks, which may significantly affect their approximation performances. Three HS methods, the original version and two new variations recently proposed by the authors of the present paper, are applied here to optimize the weights in the BP neural networks for the classification of the epileptic EEG signals. Simulations have demonstrated that the classification accuracy of the BP neural networks can be remarkably improved by the HS method-based training.
Keywords
- Harmony Search (HS) method
- ElectroEncep
How to Cite
Xiao-Zhi Gao, Jing Wang, Jarno M. A. Tanskanen, Rongfang Bie, Xiaolei Wang, Ping Guo, and Kai Zenger, "Optimal Classification of Epileptic EEG Signals Using Neural Networks and Harmony Search Methods," Journal of Software, vol. 9, no. 1, pp. 230-239, 2014. https://doi.org/ 10.4304/jsw.9.1.230-239
Copyright & License
Copyright © 2014 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).