EEG Datasets for Seizure Detection and Prediction - A Review.

2023-02-05

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

classification

electroencephalography

machine learning

Journal Article

0000-0001-7444-1405

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0000-0003-4638-9579

0000-0002-7855-7066

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0000-0001-7310-276X

0000-0002-5108-6348

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10.1002/epi4.12704

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.

link

Epilepsia Open 2023-06; 8(2)

Epilepsia Open

2470-9239