Novel ML-Based Algorithm for Detecting Seizures from Single-Channel EEG
| dc.contributor.author | M Dweiri, Yazan | |
| dc.contributor.author | K Al-Omary, Taqwa | |
| dc.date.accessioned | 2025-11-25T09:21:41Z | |
| dc.date.available | 2025-11-25T09:21:41Z | |
| dc.date.issued | 2024 | |
| dc.description | Epilepsy is one of the brain diseases that is gaining attention due to its spread and the extent of its impact on the daily lives of patients. According to the WHO, by 2031, the world is aiming to increase countries’ coverage of epilepsy services to 50% coverage in 2021. The appropriate diagnosis and treatment of people with epilepsy is estimated to allow 70% of these patients to have seizure-free lives. The diagnosis of epilepsy relies on neurological examination and observations of symptoms. The investigation of the EEG is assigned as the confirmatory tool [1], besides examining some biomarkers obtained from ECG and sleep studies. Brain imaging using CT and MRI is also utilized in the diagnosis and assessment of epilepsy and seizures [2]. | |
| dc.description.abstract | 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. Keywords: seizure classification, portable epilepsy monitoring, machine learning | |
| dc.identifier.citation | Dweiri, Y. M., & Al-Omary, T. K. (2024). Novel ML-Based algorithm for detecting seizures from single-channel EEG. NeuroSci, 5(1), 59-70. | |
| dc.identifier.doi | https://doi.org/10.3390/neurosci5010004 | |
| dc.identifier.uri | https://repository.adu.ac.ae/handle/1/7810 | |
| dc.language.iso | en | |
| dc.publisher | Multidisciplinary Digital Publishing Institute (MDPI) | |
| dc.title | Novel ML-Based Algorithm for Detecting Seizures from Single-Channel EEG | |
| dc.type | Article |