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

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Ateke Goshvarpour 1,* Hossein Ebrahimnezhad 2 Atefeh Goshvarpour 1

1. Computational Neuroscience Laboratory, Department of Biomedical Engineering, Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran

2. Department of Electrical Engineering, Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran

* Corresponding author.


Received: 19 Jan. 2013 / Revised: 10 Feb. 2013 / Accepted: 2 Mar. 2013 / Published: 8 May 2013

Index Terms

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


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.

Cite This Paper

Ateke Goshvarpour, Hossein Ebrahimnezhad, Atefeh Goshvarpour, "Classification of Epileptic EEG Signals using Time-Delay Neural Networks and Probabilistic Neural Networks", International Journal of Information Engineering and Electronic Business(IJIEEB), vol.5, no.1, pp.59-67, 2013. DOI:10.5815/ijieeb.2013.01.07


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