Novel ML-Based Algorithm for Detecting Seizures from Single-Channel EEG

There is a need for seizure classification based on EEG signals that can be implemented with a portable device for in-home continuous minoring of epilepsy. In this study, we developed a novel machine learning algorithm for seizure detection suitable for wearable systems. Extreme gradient boosting (X...

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Bibliographic Details
Published in:NeuroSci 2024-03, Vol.5 (1), p.59-70
Main Authors: Dweiri, Yazan M, Al-Omary, Taqwa K
Format: Article
Language:English
Subjects:
machine learning
portable epilepsy monitoring
seizure classification
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Summary:There is a need for seizure classification based on EEG signals that can be implemented with a portable device for in-home continuous minoring of epilepsy. In this study, we developed a novel machine learning algorithm for seizure detection suitable for wearable systems. Extreme gradient boosting (XGBoost) was implemented to classify seizures from single-channel EEG obtained from an open-source CHB-MIT database. The results of classifying 1-s EEG segments are shown to be sufficient to obtain the information needed for seizure detection and achieve a high seizure sensitivity of up to 89% with low computational cost. This algorithm can be impeded in single-channel EEG systems that use in- or around-the-ear electrodes for continuous seizure monitoring at home.
ISSN:2673-4087
2673-4087
DOI:10.3390/neurosci5010004