doi: 10.7763/IJMLC.2012.V2.231
EEG Signals Classification by Using an Ensemble TPUnit Neural Networks for the Diagnosis of Epilepsy
,
Tadaaki Shimizu
,
Maiya Hori ,
Yoshio Iwai ,
- Department of Information and Electronics Graduate school of Engineering, Tottori University, 4-101, Koyama-minani Tottori city, Japan.
Abstract
The electroencephalogram (EEG) is necessary for the diagnosis of epilepsy. To make a diagnosis of epilepsy exactly, a full EEG recording for a long stretch of time is needed. The observation for a long record is a big burden for a doctor. To reduce this burden, a computer aid is important. This paper presented classifications of EEG patterns using the ensemble TPunit NNs for the diagnosis of epilepsy. The classification accuracy rates of the proposed classifiers were found to be higher than that of stand alone neural network. In addition, the classification accuracy was higher than previous study. The ensemble of the TPUnit neural networks is highly effective in classification problem.
Keywords
- Neural network
- epilepsy
- EEG
- TPUnit
How to Cite
Hiroki Yoshimura, Tadaaki Shimizu, Maiya Hori, Yoshio Iwai, and Satoru Kishida, "EEG Signals Classification by Using an Ensemble TPUnit Neural Networks for the Diagnosis of Epilepsy," International Journal of Machine Learning, vol. 2, no. 6, pp. 758-761, 2012. https://doi.org/10.7763/IJMLC.2012.V2.231
Copyright & License
Copyright © 2012 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).