Application of Clustering Techniques on Statistical Features of EEG Signals for Seizure Detection

Epileptic seizure is the most predominant brain disorder that has affected at least 1% of the world's population and higher numbers are affected in the underdeveloped countries. The best marker for diagnosis of seizure is to analyse the EEG signals. The statistical parameters of EEG signals vary when there is an occurrence of seizure. In this paper the statistical parameter interquatile range and Mean absolute deviation are taken as features and are classified using unsupervised learning algorithms (clustering techniques). K-Mean, K-Centers, Parzen Classifier, K-Nearest Neighbour, Gaussian mixture model, and Naive Bayes classifier are put to test to find out the best suited algorithm. Among the methods used K-Nearest Neighbour gives the maximum accuracy of 100%, sensitivity and specificity of 1 for the given data followed by Parzen classifier with 98.02% of accuracy, and has a sensitivity of 0.96, specificity of 1. The Naieve bayes classifier with 96.71% of accuracy is the fastest algorithm with 0.019 ms and hence can be used for real time applications.