In recent decades, an epileptic seizure is a neurological disorder, which is commonly detected from intracranial Electroencephalogram (iEEG) signals. However, the visual interpretation and inspection of iEEG signal is subjective variability, a time-consuming mechanism, slow and vulnerable to errors. In this research article, an automated epileptic seizure detection model is proposed to highlight the above-mentioned concerns. The proposed automated model integrates the Reconstruction Independent Component Analysis (RICA) and Long Short Term Memory (LSTM) for seizure detection. In the proposed model, RICA is utilized to extract the features from the normalized iEEG signals, and then the obtained feature vectors are fed to the LSTM network for classification, which effectively classifies inter-ictal and ictal iEEG signals. This experimental outcome showed that the proposed RICA-LSTM model achieved an accuracy of 98.92%, sensitivity of 99.01%, specificity of 98.68%, balanced accuracy of 99.24%, and f-score of 98.25% in epileptic seizure recognition on the SWEC-ETHZ iEEG database, which is better compared to the conventional machine learning classifiers.
iEEG based Epileptic Seizure Detection using Reconstruction Independent Component Analysis and Long Short Term Memory Network