Mr CRAIG STEWART c.stewart35@rgu.ac.uk
Research Student
Mr CRAIG STEWART c.stewart35@rgu.ac.uk
Research Student
Wai Keung Fung
Dr Nazila Fough n.fough1@rgu.ac.uk
Lecturer
Professor Radhakrishna Prabhu r.prabhu@rgu.ac.uk
Professor
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.
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 |
STEWART 2023 Extremely random forest (AAM)
(263 Kb)
PDF
Automated tonic-clonic seizure detection using random forests and spectral analysis on electroencephalography data.
(2022)
Presentation / Conference Contribution
An investigation into routing protocols for real-time sensing of subsurface oil wells.
(2022)
Presentation / Conference Contribution
A simulation into the physical and network layers of optical communication network for the subsea video surveillance of illicit activity.
(2022)
Presentation / Conference Contribution
Performance and energy modelling for a low energy acoustic network for the underwater Internet of Things.
(2023)
Presentation / Conference Contribution
Fuzzy logic, edge enabled underwater video surveillance through partially wireless optical communication.
(2023)
Presentation / Conference Contribution
About OpenAIR@RGU
Administrator e-mail: publications@rgu.ac.uk
This application uses the following open-source libraries:
Apache License Version 2.0 (http://www.apache.org/licenses/)
Apache License Version 2.0 (http://www.apache.org/licenses/)
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2025
Advanced Search