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A combination of statistical parameters for the detection of epilepsy and EEG classification using ANN and KNN classifier

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Abstract

Electrical activity of the brain reads through the technique called as electroencephalography for brain disorder like epilepsy. Epileptical signal is extracted from EEG signal through characteristics defined by statistical parameter like expected activity measurement, sample entropy and Higuchi fractal dimension as an input to a classifier. This paper works on the classification approach of EEG signal into healthy, inter-ictal and ictal signal using k-nearest neighbor and artificial neural network classifier according to the statistical parameter. Accuracy, sensitivity, selectivity, specificity and average detection rate are the performance parameter derived from both the classifier for comparison between k-NN and ANN classifier and also for detection of epilepsy with reduced sets of parameter.

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  1. Department of Electronics and Communication, MANIT, Bhopal, India

    Hemant Choubey & Alpana Pandey

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  1. Hemant Choubey
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  2. Alpana Pandey
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Correspondence to Hemant Choubey.

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Choubey, H., Pandey, A. A combination of statistical parameters for the detection of epilepsy and EEG classification using ANN and KNN classifier. SIViP 15, 475–483 (2021). https://doi.org/10.1007/s11760-020-01767-4

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  • DOI: https://doi.org/10.1007/s11760-020-01767-4

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