Version 1
: Received: 26 February 2024 / Approved: 26 February 2024 / Online: 26 February 2024 (11:48:02 CET)
How to cite:
Bhattacharya, S.; Bennett, A.; Kriukova, K.; Alba, C.; Duncan, D. A Machine Learning Approach to Preictal Phase Detection in EEG. Preprints2024, 2024021441. https://doi.org/10.20944/preprints202402.1441.v1
Bhattacharya, S.; Bennett, A.; Kriukova, K.; Alba, C.; Duncan, D. A Machine Learning Approach to Preictal Phase Detection in EEG. Preprints 2024, 2024021441. https://doi.org/10.20944/preprints202402.1441.v1
Bhattacharya, S.; Bennett, A.; Kriukova, K.; Alba, C.; Duncan, D. A Machine Learning Approach to Preictal Phase Detection in EEG. Preprints2024, 2024021441. https://doi.org/10.20944/preprints202402.1441.v1
APA Style
Bhattacharya, S., Bennett, A., Kriukova, K., Alba, C., & Duncan, D. (2024). A Machine Learning Approach to Preictal Phase Detection in EEG. Preprints. https://doi.org/10.20944/preprints202402.1441.v1
Chicago/Turabian Style
Bhattacharya, S., Celina Alba and Dominique Duncan. 2024 "A Machine Learning Approach to Preictal Phase Detection in EEG" Preprints. https://doi.org/10.20944/preprints202402.1441.v1
Abstract
Epilepsy is a common neurological condition, typically diagnosed using Electroencephalogram (EEG). Large scale EEG datasets have recently been made publicly available, allowing the use of advanced Machine Learning (ML) algorithms to analyze EEG patterns associated with epilepsy. While most existing studies focus on identifying seizures in the EEG, few have tried to identify preictal EEG segments. Identifying preictal EEG segments are not only useful in developing early warning systems but also helps inform the course of treatment for the patient. In this study, we propose to represent EEG segments as images, instead of time-series data, and identify preictal EEG segments using a preexisting ML algorithm (YOLOv8) designed for image processing. Multiplexed images (containing the original EEG signal represented on a 2D grid, kurtosis, and spectral entropy) achieve the best accuracy of 95.75% on the dataset while images just containing the EEG signal result in an accuracy of 91.25%. Using only spectrograms, generated from the original EEG signal, results in an accuracy of 90.15%.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
