Research Area:  Machine Learning

Abstract:

This paper provides a comprehensive analysis of the available EEG datasets that are used for epilepsy prediction systems, including Melbourne, CHB-MIT, American Epilepsy Society, Bonn, and European Epilepsy datasets. These datasets are compared in terms of the sampling rate, number of patients, recording time, number of channels, artifacts, and types of EEG signals. We also provide details on the challenges of using one dataset over the others in predicting epilepsy. Subsequently, we compare the performance of various machine learning models that use these datasets for epileptic seizure prediction. This is the first work that provides a comprehensive analysis of various EEG datasets and should be of great importance for researchers in EEG-based systems for epileptic seizure prediction.

Keywords:  

Author(s) Name:  Rihat Rahman; Shiva Maleki Varnosfaderani; Omar Makke; Nabil J. Sarhan; Eishi Asano; Aimee Luat; Mohammad Alhawari

Publisher name:  IEEE

DOI:  10.1109/ISCAS51556.2021.9401766

Volume Information:  

Paper Link:   https://ieeexplore.ieee.org/abstract/document/9401766