ABSTRACT Electroencephalogram (EEG) comprises valuable details related to the different physiological state of the brain. In this paper, a framework is offered for detecting the epileptic seizures from EEG data recorded from normal subjects and epileptic patients. This framework is based on a discrete wavelet transform (DWT) analysis of EEG signals using linear and nonlinear classifiers. The performance of the 14 different combinations of two-class epilepsy detection is studied using naïve Bayes (NB) and k-nearest neighbor (k-NN) classifiers for the derived statistical features from DWT. It has been found that the NB classifier performs better and shows an accuracy of 100% for the individual and combined statistical features derived from the DWT values of normal eyes open and epileptic EEG data provided by the University of Bonn, Germany. It has been found that the computation time of NB classifier is lesser than k-NN to provide better accuracy. So, the detection of an epileptic seizure based on DWT statistical features using NB classifiers is more suitable in real time for a reliable, automatic epileptic seizure detection system to enhance the patient's care and the quality of life. INDEX TERMS Electroencephalograms (EEG), epilepsy, discrete wavelet transform (DWT), naïve Bayes (NB), k-nearest neighbor (k-NN).
The authors in REF primarily rely on an analysis of EEG signals by making use of discrete wavelet transforms (DWT) to decompose the EEG data into different sub-bands, and then extract the statistical features.
DWT Based Detection of Epileptic Seizure From EEG Signals Using Naive Bayes and k-NN Classifiers
IEEE Access
IEEE Access
Computer Science