Epileptic Seizure Detection Using EEG Signal Based LSTM Models

In this research, a comparative LSTM model analysis is discussed for the recognition of epileptic seizure from EEG signals. The architectures of LSTM are followed with Vanilla, Stacked, and Bi-directional for epileptic seizure detection in raw EEG signals. The advantages of applying LSTM directly to the raw EEG signal are to perform features’ extraction and speed up the training by substituting the original signals with shorter sequences of feature vectors. In this research work, model loss is analyzed to evaluate the performance of different LSTM models. The proposed approach is applied in the EEG dataset from Bonn University and results show that the performance of the bi-directional LSTM is better than the others.