Supervised and Unsupervised Deep Learning Approaches for EEG Seizure Prediction
This research addresses the critical need for seizure prediction to reduce risks for over 50 million people with epilepsy, though it appears incremental as it builds on existing deep learning methods.
The paper tackled the problem of predicting epileptic seizures by detecting preictal EEG from normal EEG using supervised and unsupervised deep learning approaches, finding that both are feasible but performance varies by patient and method.
Epilepsy affects more than 50 million people worldwide, making it one of the world's most prevalent neurological diseases. The main symptom of epilepsy is seizures, which occur abruptly and can cause serious injury or death. The ability to predict the occurrence of an epileptic seizure could alleviate many risks and stresses people with epilepsy face. We formulate the problem of detecting preictal (or pre-seizure) with reference to normal EEG as a precursor to incoming seizure. To this end, we developed several supervised deep learning approaches to identify preictal EEG from normal EEG. We further develop novel unsupervised deep learning approaches to train the models on only normal EEG, and detecting pre-seizure EEG as an anomalous event. These deep learning models were trained and evaluated on two large EEG seizure datasets in a person-specific manner. We found that both supervised and unsupervised approaches are feasible; however, their performance varies depending on the patient, approach and architecture. This new line of research has the potential to develop therapeutic interventions and save human lives.