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

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 usingk-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 betweenk-NN and ANN classifier and also for detection of epilepsy with reduced sets of parameter.

Automated summary

This paper focuses on classifying EEG signals into healthy, inter-ictal, and ictal signals using statistical parameters, and accurately detecting epilepsy with reduced sets of parameters. The study compares the performance of k-nearest neighbor and artificial neural network classifiers for this classification task.