Classification of EEG signals for detection of epileptic seizure activities based on feature extraction from brain maps using image processing algorithms

This study presents a novel feature extraction approach based on image processing algorithms for the automated detection of epileptic seizure activities in brain map representation of electroencephalography (EEG) signal using an efficient classification technique. The proposed technique uses independent component analysis to extract independent components (ICs) from the EEG signal and each extracted IC is transformed into an image termed as brain maps. Two feature extraction techniques namely closed neighbourhood gradient pattern (CNGP) and combined texture pattern (CTP) are propounded for automatic elimination of artefact brain maps. The extracted features are fed into the least square support vector machine (LSSVM) for automatic detection of epileptic brain maps. Extensive experimental result over the existing image processing techniques in literature demonstrates that the texture pattern representations of CNGP and CTP are improved to obtain better features to enhance the performance of texture classification. The obtained result shows that the LSSVM classifier with Gaussian RBF kernel is able to detect the epileptic brain map with a high accuracy rate. The results are reliable and it assists the neurologist to diagnose epileptic signals effortlessly by visually locating the brain area being affected by seizure activities.