A 1-D CNN-FCM model for the classification of epileptic seizure disorders

The seizure is an unusual event of the brain, which leads to the second most common disease of the brain called epilepsy. Electroencephalography (EEG) has the potential to provide insight into the diagnosis of seizure. Our objective is to explore the practical efficiency of the convolutional neural network (CNN) in detecting seizures using EEG signal. A novel CNN-FCM architecture is proposed to classify the seizure signals. The conventional CNN is modified with Fuzzy C-means (FCM) clustering algorithm. The competency of the clustering method is confirmed with the cluster validity index (CVI) parameters such as partition entropy (PE), partition coefficient (PC) and the Xie-Beni index (XB). The efficiency of proposed CNN-FCM architecture is validated and confirmed by considering the standard classification parameters such as accuracy, sensitivity, specificity and F-measure. The two different seizure EEG datasets are utilized to examine the proposed system. The proposed CNN-FCM architecture achieved the classification accuracy of 98.33% and outperformed with other existing deep learning methods, achieving a less computational time of 0.3286 s in classification. The performance outcomes exhibit the efficiency of the proposed one-dimensional CNN-FCM in the diagnosis of epilepsy. In contrast to other existing automatic seizure detection techniques, the CNN-FCM architecture can perform real-time seizure detection and classification.