A deep learning network with minimal set of features for classification of ictal, interictal, and preictal EEG states

摘要

Epilepsy is a neurological disorder that affects a wide population of the world. It is categorized by a number of repeated seizures. The electroencephalogram (EEG) has been a most powerful tool in the diagnosis of epilepsy. This work proposes a long short-term memory (LSTM) network with only one feature, characterizing cross-frequency correlation, for the classification of different EEG states, viz., ictal (during seizure), interictal (between seizures) and preictal (before seizure). The LSTM network has been assessed using open CHB-MIT scalp EEG database consisting of 983 hours’ long-term EEG recordings. The accuracy of 95.71% was achieved for preictal duration of 30 minutes (mins) and when EEG of 1 hour (hr) before and after the seizures has not been included in interictal state. The results of the evaluation suggest the use of proposed network with minimal set of features in seizure detection as well as seizure prediction.