EEG Signals Classification by Using an Ensemble TPUnit Neural Networks for the Diagnosis of Epilepsy

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.

Index Terms—Neural network, epilepsy, EEG, TPUnit.

The authors are with the Department of Information and Electronics Graduate school of Engineering, Tottori University, 4-101, Koyama-minani Tottori city, Japan (e-mail: yosimura@ike.tottori-u.ac.jp; tadaaki@ ike.tottori-u.ac.jp; hori@ ike.tottori-u.ac.jp; iwai@ ike.tottori-u.ac.jp).

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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 and Computing vol.2, no. 6, pp. 758-761, 2012.