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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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