A novel epileptic seizure detection system using scalp EEG signals based on hybrid CNN-SVM classifier

Epilepsy is a neurological disorder that affects more than 2% of the world's population. Encephalography (EEG) is a commonly clinical tool used for the diagnosis of epilepsy. However, traditional approaches based on visual inspection of EEG signals are tedious and complex. Thus, several automatic seizure detection approaches based on machine learning techniques have been proposed. In this study, a hybrid model for the detection of epileptic seizure is proposed, where convolutional neural network (CNN) is used for automatic feature extraction of EEG signals and support vector machines (SVM) is used for epileptic seizure classification. The proposed approach was evaluated using the Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) dataset. Experimental results showed that the accuracy of the combined CNN-SVM model outperforms the CNN baseline model. The proposed approach provides a substantial increase in seizure prediction performance in terms of sensitivity compared to both classical machine learning approaches and CNN model that have been presented in the previous studies.