A Robust Deep Learning Approach for Automatic Seizure Detection
Detecting epileptic seizure through analysis of the electroencephalography (EEG) signal becomes a standard method for the diagnosis of epilepsy. In a manual way, monitoring of long term EEG is tedious and error prone. Therefore, a reliable automatic seizure detection method is desirable. A critical challenge to automatic seizure detection is that seizure morphologies exhibit considerable variabilities. In order to capture essential seizure patterns, this paper leverages an attention mechanism and a bidirectional long short-term memory (BiLSTM) model to exploit both spatially and temporally discriminating features and account for seizure variabilities. The attention mechanism is to capture spatial features more effectively according to the contributions of brain areas to seizures. The BiLSTM model is to extract more discriminating temporal features in the forward and the backward directions. By accounting for both spatial and temporal variations of seizures, the proposed method is more robust across subjects. The testing results over the noisy real data of CHB-MIT show that the proposed method outperforms the current state-of-the-art methods. In both mixing-patients and cross-patient experiments, the average sensitivity and specificity are both higher while their corresponding standard deviations are lower than the methods in comparison.