| Autor: |
Yazan M. Dweiri, Taqwa K. Al-Omary |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
NeuroSci, Vol 5, Iss 1, Pp 59-70 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2673-4087 |
| DOI: |
10.3390/neurosci5010004 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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