Automatic diagnosis of epileptic and Non-Epileptic seizures using a hybrid dataset

To diagnose epilepsy at the first stage, clinicians can use scalp EEG(sEEG) as a safe option. However, the literature has no sEEG-based automatic diagnosis of epileptic (ES) and non-Epileptic (NES) Seizures. On the other hand, the existing benchmark datasets are unsuitable for an sEEG-based model. Thus, we created an sEEG-based dataset for both classes by merging the Bonn and CHB-MIT datasets. We performed a critical channel selection, comparing the stability of four classifiers across different channels. The results demonstrate that when utilizing Channel 1, the NB classifier achieves 98.97% accuracy, 99.71% specificity, and 97.73% sensitivity.