TY - GEN
T1 - Comprehensive analysis of EEG datasets for epileptic seizure prediction
AU - Rahman, Rihat
AU - Varnosfaderani, Shiva Maleki
AU - Makke, Omar
AU - Sarhan, Nabil J.
AU - Asano, Eishi
AU - Luat, Aimee
AU - Alhawari, Mohammad
N1 - Funding Information:
This work was supported by Richard Barber Interdisciplinary Research Program. Rihat Rahman and Shiva Maleki Varnosfaderani contributed equally to this work. (Corresponding author: Mohammad Alhawari.)
Publisher Copyright:
© 2021 IEEE
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - EEG datasets
KW - Electroencephalography (EEG)
KW - Epilepsy
KW - Epilepsy prediction system
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85109044821&partnerID=8YFLogxK
U2 - 10.1109/ISCAS51556.2021.9401766
DO - 10.1109/ISCAS51556.2021.9401766
M3 - Conference contribution
AN - SCOPUS:85109044821
T3 - Proceedings - IEEE International Symposium on Circuits and Systems
BT - 2021 IEEE International Symposium on Circuits and Systems, ISCAS 2021 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 22 May 2021 through 28 May 2021
ER -