Identifying signal-dependent information about the preictal state: A comparison across ECoG, EEG and EKG using deep learning.
Journal:
EBioMedicine
Published Date:
Jul 1, 2019
Abstract
BACKGROUND: The inability to reliably assess seizure risk is a major burden for epilepsy patients and prevents developing better treatments. Recent advances have paved the way for increasingly accurate seizure preictal state detection algorithms, primarily using electrocorticography (ECoG). To develop seizure forecasting for broad clinical and ambulatory use, however, less complex and invasive modalities are needed. Algorithms using scalp electroencephalography (EEG) and electrocardiography (EKG) have also achieved better than chance performance. But it remains unknown how much preictal information is in ECoG versus modalities amenable to everyday use - such as EKG and single channel EEG - and how to optimally extract that preictal information for seizure prediction.