Epileptic EEG detection using neural networks and post-classification

Authors:
L. M. Patnaik;Ohil K. Manyam
Affiliations:
Computational Neurobiology Group, Supercomputer Education and Research Centre, Indian Institute of Science, Bangalore 560012, India;Department of Electronics and Communication Engineering, National Institute of Technology Karnataka, Surathkal, Srinivasnagar 575025, India
Venue:
Computer Methods and Programs in Biomedicine
Year:
2008

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Abstract

Electroencephalogram (EEG) has established itself as an important means of identifying and analyzing epileptic seizure activity in humans. In most cases, identification of the epileptic EEG signal is done manually by skilled professionals, who are small in number. In this paper, we try to automate the detection process. We use wavelet transform for feature extraction and obtain statistical parameters from the decomposed wavelet co-efficients. A feed-forward backpropagating artificial neural network (ANN) is used for the classification. We use genetic algorithm for choosing the training set and also implement a post-classification stage using harmonic weights to increase the accuracy. Average specificity of 99.19%, sensitivity of 91.29% and selectivity of 91.14% are obtained.