An Efficient Feature Selection and Explainable Classification Method for EEG-based Epileptic Seizure Detection

Epilepsy is a prevalent neurological disorder that poses life-threatening emergencies. Early electroencephalogram (EEG) seizure detection can mitigate the risks and aid in the treatment of patients with epilepsy. Electroencephalogram (EEG) based automatic epileptic seizure (ES) detection has significant applications in epilepsy treatment and medical diagnosis. Therefore, this paper presents an innovative framework for automatic ES detection using coefficient and distance correlation feature selection algorithms, a Bagged Tree-based classifier (BTBC), and Explainable artificial intelligence (XAI). Initially, the Butterworth filter is employed to eliminate various artifacts, and the discrete wavelet transform (DWT) is used to decompose the EEG signals and extract 24 eigenvalue features of the statistical time domain (STD) as linear and Fractal dimension-based non-linear (FD-NL). The optimal features are then identified through correlation coefficients with $P-value$ and distance correlation analysis. These features are subsequently utilized by the BTBC. The model provides superior performance in mitigating overfitting issues and improves the average accuracy by 4\% using (CD, E), (AB, CD, E), and (A, B) combination sets as compared to other machine learning (ML) models using well-known Bonn and UCI-EEG benchmark datasets. Finally, SHapley additive exPlanation (SHAP) was used as an Explainable AI (XAI) to interpret and explain the decision-making process of the proposed model. The results highlight the framework's capability to accurately classify ES, thereby improving the diagnosis process in patients with brain dysfunctions.