A Robust Deep Learning Approach for Automatic Classification of Seizures Against Non-seizures
This work addresses the need for reliable, automated seizure detection in epilepsy diagnosis, which is currently manual and error-prone, representing an incremental improvement with specific gains in robustness across patients.
The paper tackled the problem of automatic seizure classification from EEG signals by proposing a method using attention mechanisms and BiLSTM to handle seizure variabilities, achieving an average sensitivity of 87.00%, specificity of 88.60%, and precision of 88.63% on the CHB-MIT dataset, outperforming state-of-the-art methods.
Identifying epileptic seizures through analysis of the electroencephalography (EEG) signal becomes a standard method for the diagnosis of epilepsy. Manual seizure identification on EEG by trained neurologists is time-consuming, labor-intensive and error-prone, and a reliable automatic seizure/non-seizure classification method is needed. One of the challenges in automatic seizure/non-seizure classification 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) to exploit both spatial and temporal discriminating features and overcome seizure variabilities. The attention mechanism is to capture spatial features according to the contributions of different brain regions to seizures. The BiLSTM is to extract discriminating temporal features in the forward and the backward directions. Cross-validation experiments and cross-patient experiments over the noisy data of CHB-MIT are performed to evaluate our proposed approach. The obtained average sensitivity of 87.00%, specificity of 88.60% and precision of 88.63% in cross-validation experiments are higher than using the current state-of-the-art methods, and the standard deviations of our approach are lower. The evaluation results of cross-patient experiments indicate that, our approach has better performance compared with the current state-of-the-art methods and is more robust across patients.