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
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: email@example.com; tadaaki@ ike.tottori-u.ac.jp; hori@ ike.tottori-u.ac.jp; iwai@ ike.tottori-u.ac.jp).
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.