Automatic seizure detection using neutrosophic classifier

Seizures are the most common brain dysfunction. EEG is required for their detection and treatment initially. Studies proved that if seizures are detected at their early stage, proper and effective treatment can be given to patients. Automatic detection of seizures using the EEG signal was a very powerful area of research during the last decade. Various techniques have been proposed in the literature for feature extraction and classification of recorded EEG signals for seizure detection. However, to achieve reliable performance, some challenges in this area need to be addressed. In this work, an algorithm for seizure detection has been proposed, which is a combination of frequency-domain features and neutrosophic logic-based k-means nearest neighbor (NL-k-NN) classifier. An EEG database, collected at All India Institutes of Medical Sciences (AIIMS), New Delhi, has been used to test the performance of the proposed algorithm. The consistency in the performance of the proposed algorithm has been checked by applying it to the well-known Bonn University and CHB-MIT scalp EEG datasets. The classification accuracies of 98.16%, 100%, and 89.06% were achieved when the proposed algorithm was tested with AIIMS, Bonn University, and CHB-MIT datasets, respectively. The main contribution of this study is that a novel neutrosophic classifier is proposed in the field of seizure detection, for improvement in reliability and precision. The accuracy of the NL-k-NN classifier has further been established by comparing it with the reported results of linear discriminant analysis (LDA), support vector machine (SVM), and traditional k-NN classifiers.