Research Area:  Machine Learning

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.

Keywords:  

Author(s) Name:  Hemant Choubey & Alpana Pandey

Publisher name:  Springer

DOI:  10.1007/s11760-020-01767-4

Volume Information:  volume 15, pages475–483 (2021)

Paper Link:   https://link.springer.com/article/10.1007/s11760-020-01767-4