Version 1
: Received: 21 July 2023 / Approved: 24 July 2023 / Online: 25 July 2023 (03:25:03 CEST)
How to cite:
Dutta, K. K.; Manohar, P.; K, I. Prediction of Seizure Stages Based on Electroencephalography Signals and Machine Learning. Preprints2023, 2023071606. https://doi.org/10.20944/preprints202307.1606.v1
Dutta, K. K.; Manohar, P.; K, I. Prediction of Seizure Stages Based on Electroencephalography Signals and Machine Learning. Preprints 2023, 2023071606. https://doi.org/10.20944/preprints202307.1606.v1
Dutta, K. K.; Manohar, P.; K, I. Prediction of Seizure Stages Based on Electroencephalography Signals and Machine Learning. Preprints2023, 2023071606. https://doi.org/10.20944/preprints202307.1606.v1
APA Style
Dutta, K. K., Manohar, P., & K, I. (2023). Prediction of Seizure Stages Based on Electroencephalography Signals and Machine Learning. Preprints. https://doi.org/10.20944/preprints202307.1606.v1
Chicago/Turabian Style
Dutta, K. K., Premila Manohar and Indira K. 2023 "Prediction of Seizure Stages Based on Electroencephalography Signals and Machine Learning" Preprints. https://doi.org/10.20944/preprints202307.1606.v1
Abstract
Electroencephalography (EEG) is essential for tracking brain activity and identifying seizure effects. However, epileptic behaviour can only be detected after a specialist has carefully analysed all EEG recordings along with a proper history of the patient. A skilled physician is required for the right epilepsy diagnosis and therapy. But most of the time, patients visit the clinician in the interictal stage with no proper history documented. Therefore, it was essential to the automatic prediction of stages of seizure. K nearest neighbours (KNN) and random forest (RF) models using raw EEG signals, preictal, ictal, postictal, and interictal stages were identified in this study. The possibility of these characteristics is explored by examining how well time-domain signals work in the prediction of epileptic stages using intracranial EEG datasets from Freiburg Hospital (FH), Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT), and Temple University Hospital (TUHEEG). To test the viability of this approach, two different types of simulations were carried out on three binary classifications (interictal vs. preictal, interictal vs. ictal, preictal vs. postictal, and interictal vs. postictal), and one four-class problem (interictal vs. preictal vs. ictal vs. postictal) was performed for each model. The average accuracy when using time-domain signals in the FH database was 90.5% and 75.0%; CHB-MIT was 92.87% and 75.9%; and TUHEEG was 94.46% and 76.8%, respectively, for the KNN and RF models.
Biology and Life Sciences, Neuroscience and Neurology
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.
