In this paper, an ensemble of radial basis function neural networks (RBFNs) optimized by differential evolution (DE) (DE-RBFNs) is presented for identification of epileptic seizure by analyzing the electroencephalography (EEG) signal. The ensemble is based on the bagging approach and the base learner is DE-RBFNs. The EEGs are decomposed with wavelet transform into different sub-bands and some statistical information is extracted from the wavelet coefficients to supply as the input to ensemble of DE-RBFNs. A benchmark publicly available dataset is used to evaluate the proposed method. The classification results confirm that the proposed ensemble of DE-RBFNs has greater potentiality to identify the epileptic disorders.
|Number of pages||12|
|Journal||Procedia Computer Science|
|Publication status||Published - 2013|
|Event||4th International Conference on Computational Systems-Biology and Bioinformatics, CSBio 2013 - Seoul, Korea, Republic of|
Duration: 2013 Nov 7 → 2013 Nov 9
Bibliographical noteFunding Information:
Authors are gratefully acknowledge the support of the Original Technology Research Program for Brain Science through the National Research Foundation (NRF) of Korea (NRF: 2010-0018948) funded by the Ministry of Education, Science, and Technology.
All Science Journal Classification (ASJC) codes
- Computer Science(all)