A deep learning approach to identify seizure-prone and normal patients from their EEG records

Various learning models distinguish between an electroencephalogram (EEG) record of a normal patient and one having a seizure. In this paper, we propose a deep-learning based short-term memory (LSTM) model to identify whether an EEG record belongs to a seizure-prone patient with a non-seizure record or to a normal patient. The study builds on two datasets, namely the TUH Abnormal EEG Corpus (TUAB) and the TUH EEG Seizure Corpus (TUSZ) including the classified EEG records for seizure-prone and normal patients. We conducted experiments on both imbalanced and balanced datasets and show results using an LSTM model. We observed that the model performs consistently in both balanced and imbalanced cases using only 5 seconds of EEG data from the patient records. We show that our proposed LSTM model gives test accuracies up to 99.84% in case of 2-class classification between the non-seizure and normal classes and up to 98.87% in case of 3-class classification among non-seizure, seizure, and normal classes. This provides a basis for making improved temporal predictions about the occurrences of seizures.