Channel-annotated deep learning for enhanced interpretability in EEG-based seizure detection

Abstract

Currently, electroencephalogram (EEG) provides critical data to support the diagnosis of epilepsy through the identification of seizure events. The review process is undertaken by clinicians or EEG specialists and is labour-intensive, especially for long-term EEG recordings. Deep learning (DL) has been proposed to automate and expedite the seizure review and annotation process, providing superior performance when compared to traditional machine learning (ML) methods. However, DL algorithms lack interpretability which is a crucial factor for clinical adoption. Consequently, the “black-box” nature of these DL algorithms limits the transparency of these algorithms, preventing clinicians from ha..