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Extremely random forest based automatic tonic-clonic seizure detection using spectral analysis on electroencephalography data.

Stewart, Craig; Fung, Wai Keung; Fough, Nazila; Prabhu, Radhakrishna

Authors

Wai Keung Fung



Abstract

Machine learning proliferates society and has begun changing medicine. This report covers an investigation into how Extremely Random Forests combined with Fast Fourier Transform feature extraction performed on two-dimensional time-series Epileptic Seizure data from the Bonn/UCI dataset. It found that robust classification can take place with lower channel counts, achieving 99.81% recall, 98.8% precision and 99.35% accuracy, outperforming previous works carried into this scenario.

Citation

STEWART, C., FUNG, WAI KEUNG, FOUGH, N. and PRABHU, R. 2023. Extremely random forest based automatic tonic-clonic seizure detection using spectral analysis on electroencephalography data. In Proceedings of the 21st IEEE (Institute of Electrical and Electronics Engineers) Interregional NEWCAS conference 2023 (NEWCAS 2023), 26-28 June 2023, Edinburgh, UK. Piscataway: IEEE [online], article 10198101. Available from: https://doi.org/10.1109/NEWCAS57931.2023.10198101

Presentation Conference Type Conference Paper (published)
Conference Name 21st IEEE (Institute of Electrical and Electronics Engineers) Interregional NEWCAS conference 2023 (NEWCAS 2023)
Start Date Jun 26, 2023
End Date Jun 28, 2023
Acceptance Date Apr 12, 2023
Online Publication Date Aug 7, 2023
Publication Date Dec 31, 2023
Deposit Date Sep 4, 2023
Publicly Available Date Sep 4, 2023
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Peer Reviewed Peer Reviewed
Series ISSN 2474-9672
ISBN 9798350300246
DOI https://doi.org/10.1109/NEWCAS57931.2023.10198101
Keywords Epilepsy; Extremely random forest; Electroencephalography; Fourier transform
Public URL https://rgu-repository.worktribe.com/output/2054236

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