Interpretable Machine Learning for Epileptic Seizure Detection on the BEED Using LIME with an Ensemble Network

This study aims to identify seizures in four different stages among epileptic patients, utilizing the Bangalore Epilepsy Dataset (BEED). This dataset, which has 16 channels, was sourced from the UCI Machine Learning Repository. Initially, the data underwent preprocessing through UMAP for dimensionality reduction. This was succeeded by feature extraction via the Fast Fourier Transform (FFT), which transformed the scaled signals into the frequency domain to capture their spectral characteristics. The findings show that a two-level ensemble model surpasses the performance of leading methods, reaching an accuracy rate of 97.06%. The model’s performance was confirmed through stringent nested cross-validation, guaranteeing consistency across all dataset folds. The model’s potential for real-time deployment on Edge and Internet of Things (IoT) devices is underscored by these findings.