A real time experimental setup for classification of epilepsy risk levels

•Focused on the representation of EEG signal in lower dimension feature space using code converter.•Epoch to epoch based analysis of EEG signal is carried out for the instant epilepsy risk level classification.•Nonlinear models and neural networks are roped as a level two classifier for good classification accuracy.•High quality value and less than 1% false alarm is attained.

The objective of this paper is to analyze the performance of singular value decomposition, expectation maximization, and Elman Neural Networks in optimization of code converter outputs in the classification of epilepsy risk levels from EEG (electroencephalogram) signals. The signal parameters such as the total number of positive and negative peaks, spikes and sharp waves, their duration etc., were extracted using morphological operators and wavelet transforms. Code converters were considered as a level one classifier. Code converters were found to have a performance index and quality value of 33.26 and 12.74, respectively, which is low. Consequently, for the EEG signals of 20 patients, the post classifiers were applied across 3 epochs of 16 channels. After having made a comparative study of different architectures, SVD was found to be the best post classifier as it marked a performance index of 89.48 and a quality value of 20.62. Elman neural network also exhibits good performance metrics than SVD in the morphological operator based feature extraction method.

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