An Improved Cognitive Approach for Automated Epileptic Seizure Detection from Multichannel EEG

While electroencephalography (EEG) is useful in monitoring and diagnosing epileptic patients’ brain activity, all EEG recordings must be analyzed by an expert to detect epileptic activity. Computer-assisted epilepsy diagnosis based on electroencephalography (EEG) assessment is a useful method that uses machine learning to improve identification r ates and save clinicians time examining EEGs. Traditional approaches for analyzing EEG data for epilepsy detection are time-consuming as it involves multiple channels, thousand numbers of features to classify the data correctly. Recently, various automated seizure detection frameworks based on machine learning techniques have been suggested to replace traditional approaches. But as multichannel EEG data is chaotic, choosing optimum channels as well as features and categorizing them are still open challenges. In this paper, we have proposed one method called sequential channel optimization by which we will select the optimum channels or electrodes to classify the data. After that, we have extracted time domain, frequency domain, wavelet domain, and EMD domain features and fed the data through SVM, KNN, and Random Forest classifiers. It has been noticed that after channel optimization the accuracy has been improved and EMD features outperform all the other domains to detect epilepsy. It is also noticeable that SVM performs best with EMD features.