Deep recurrent neural network for seizure detection

Abstract

EEG is one the most effective tools used in the diagnosis of epilepsy. However, proper diagnosis of epilepsy requires the detection and analysis of epileptic seizures for a long period of time. Manual monitoring of long term EEG is tedious and costly. Therefore, a reliable automated seizure detection system is desirable. Most current state-of-the-art methods use hand crafted feature extraction and simple classification techniques, which often leads to sub-par performance due to lack of generalizability across patient dataset. Consequently, this work introduces novel deep recurrent neural network (DRNN) architecture to perform automated patient specific seizure detection using scalp EEG. We further propose a unique mapping of seizure EEG signal for efficient processing with the DRNN. This mapping allows the proposed deep architecture to simultaneously learn both temporal and spatial features of raw seizure EEG respectively. The proposed DRNN architecture is tested with long term patient specific scalp EEG data of 5 subjects with approximately 34 hours of EEG extracted from a publically available dataset. Overall, the proposed network successfully detects 100% of total seizure events with an average detection delay of ∼7.0 sec. The results demonstrate superior performance to that of the current state-of-art seizure detection methods. The proposed DRNN architecture also obtains a runtime of approximately 30ms for 1 sec segment of 18 channel EEG. The low processing time with sparse use of computing resources and superior performance make the proposed architecture appropriate for real-time use.