Seizure Detection by Analyzing EEG Signals Using Deep Learning Networks

Epileptic seizures is a well-known and the most chronic neurological disorder and it is observed highly in infants and elderly people. Symptoms of epileptic seizures are very dangerous; they lead to injuries due to falling because of jerking movements of the arms and legs that can’t be controlled. Electroencephalography (EEG) signals are prominent tools to analyze brain activities and detect seizures. Learning machines are promising in detecting seizures by analyzing EEG signals. This study proposes a customized deep learning network for seizure detection, i.e., DLN-SD utilizing EEG signals. The publicly available CHB-MIT Scalp EEG dataset is considered. It is empirically compared with pre-trained models, namely AlexNet and GoogleNet and observed that the proposed model (DLN-SD) brings 96.0% precision, 98.0% recall, and 98.0% accuracy and performs best among all three models. It is concluded that the DLN-SD shows an improvement of 15% over AlexNet and 9% over GoogleNet to detect seizures from the EEG dataset.