Please use this identifier to cite or link to this item: https://ahro.austin.org.au/austinjspui/handle/1/32151
Title: EEG Datasets for Seizure Detection and Prediction - A Review.
Austin Authors: Wong, Sheng;Simmons, Anj;Rivera-Villicana, Jessica;Barnett, Scott;Sivathamboo, Shobi;Perucca, Piero ;Ge, Zongyuan;Kwan, Patrick;Kuhlmann, Levin;Vasa, Rajesh;Mouzakis, Kon;O'Brien, Terence J
Affiliation: Applied Artificial Intelligence Institute, Deakin University, Burwood, Victoria, Australia.
Medicine (University of Melbourne)
Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia.
Department of Neurology, The Royal Melbourne Hospital, Parkville, Victoria, Australia.
Monash eResearch Centre, Monash University, Clayton, Victoria, Australia.
Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia.
Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Victoria, Australia.
Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia.
Issue Date: 5-Feb-2023
Date: 2023
Publication information: Epilepsia Open 2023-06; 8(2)
Abstract: Electroencephalogram (EEG) datasets from epilepsy patients have been used to develop seizure detection and prediction algorithms using machine learning (ML) techniques with the aim of implementing the learned model in a device. However, the format and structure of publicly available datasets are different from each other and there is a lack of guidelines on the use of these datasets. This impacts the generatability, generalisability and reproducibility of the results and findings produced by the studies. In this narrative review, we compiled and compared the different characteristics of the publicly available EEG datasets that are commonly used to develop seizure detection and prediction algorithms. We investigated the advantages and limitations of the characteristics of the EEG datasets. Based on our study, we identified 17 characteristics that make the EEG datasets unique from each other. We also briefly looked into how certain characteristics of the publicly available datasets affect the performance and outcome of a study, as well as the influences it has on the choice of ML techniques and pre-processing steps required to develop seizure detection and prediction algorithms. In conclusion, this study provides a guideline on the choice of publicly available EEG datasets to both clinicians and scientists working to develop a reproducible, generalisable and effective seizure detection and prediction algorithm.
URI: https://ahro.austin.org.au/austinjspui/handle/1/32151
DOI: 10.1002/epi4.12704
ORCID: 0000-0001-7444-1405
0000-0003-4638-9579
0000-0002-7855-7066
0000-0001-7310-276X
0000-0002-5108-6348
Journal: Epilepsia Open
PubMed URL: 36740244
ISSN: 2470-9239
Type: Journal Article
Subjects: classification
electroencephalography
machine learning
Appears in Collections:Journal articles

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