An EEG-based Automatic Classification Model for Epilepsy with Explainable Artificial Intelligence
Lan Wei, Catherine Mooney · 2024
Effective monitoring of patients’ conditions is crucial in medical practice. Machine learning methods hold promise for automating disease detection, including epilepsy. However, the opacity of these black-box models presents significant challenges. In this study, we propose a LightGBM-based automatic classification model for epilepsy trained using TUH EEG data. Ten channels were employed, and 22 features from both the time and frequency domains were estimated from each channel. The model can distinguish between normal EEG, focal epilepsy, and generalised epilepsy. We trained the model on a dataset comprising 600 adult EEG records. Five-fold cross-validation yielded a mean accuracy of 89.46% and a mean F1 score of 0.8907 on the training set. To assess the model’s generalization performance, we independently tested it on 456 EEGs, achieving an accuracy of 71.49% and a weighted F1-score of 0.7386. Furthermore, we employed permutation feature importance, SHAP and LIME, to provide explanations for the model’s decisions. This model has the potential to gain the trust of clinicians and facilitate its adoption in clinical settings.