Classification of Epileptic EEG Signals using Time-Delay Neural Networks and Probabilistic Neural Networks

Classification of Epileptic EEG Signals using Time-Delay Neural Networks and Probabilistic Neural Networks

Автор: Ateke Goshvarpour, Hossein Ebrahimnezhad, Atefeh Goshvarpour

Журнал: International Journal of Information Engineering and Electronic Business(IJIEEB) @ijieeb

Статья в выпуске: 1 vol.5, 2013 года.

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The aim of this paper is to investigate the performance of time delay neural networks (TDNNs) and the probabilistic neural networks (PNNs) trained with nonlinear features (Lyapunov exponents and Entropy) on electroencephalogram signals (EEG) in a specific pathological state. For this purpose, two types of EEG signals (normal and partial epilepsy) are analyzed. To evaluate the performance of the classifiers, mean square error (MSE) and elapsed time of each classifier are examined. The results show that TDNN with 12 neurons in hidden layer result in a lower MSE with the training time of about 19.69 second. According to the results, when the sigma values are lower than 0.56, the best performance in the proposed probabilistic neural network structure is achieved. The results of present study show that applying the nonlinear features to train these networks can serve as useful tool in classifying of the EEG signals.

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Classification, Epileptic, EEG signals, Nonlinear Features, Time-Delay Neural Networks

Короткий адрес: https://sciup.org/15013168

IDR: 15013168

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