Understanding the factors that contribute to sudden unexpected death of epilepsy (SUDEP) is crucial for preventing this fatal complication. One potential risk marker is postictal generalized electroencephalogram (EEG) suppression (PGES). However, accurately detecting the end of PGES is challenging due to physiological artifacts in real-world data. To address this, scientists have developed a hybrid approach that combines unsupervised and supervised learning techniques. By leveraging a K-means clustering model to group EEG recordings with similar artifact features, and training random forest (RF) models based on these clusters, the researchers achieved improved PGES detection performance. With leave-one-out cross-validation, their approach demonstrated an accuracy of 64.92% and 79.85% for tolerances of 5 and 10 seconds respectively. Additionally, the average predicted time distance was 8.26 seconds. These results highlight the effectiveness of the hybrid learning approach compared to existing methods. By implementing such advanced algorithms, we can better identify individuals at higher risk of SUDEP and develop targeted preventive strategies.

Sudden unexpected death of epilepsy (SUDEP) is a catastrophic and fatal complication of epilepsy and is the primary cause of mortality in those who have uncontrolled seizures. While several multifactorial processes have been implicated including cardiac, respiratory, autonomic dysfunction leading to arrhythmia, hypoxia, and cessation of cerebral and brainstem function, the mechanisms underlying SUDEP are not completely understood. Postictal generalized electroencephalogram (EEG) suppression (PGES) is a potential risk marker for SUDEP, as studies have shown that prolonged PGES was significantly associated with a higher risk of SUDEP. Automated PGES detection techniques have been developed to efficiently obtain PGES durations for SUDEP risk assessment. However, real-world data recorded in epilepsy monitoring units (EMUs) may contain high-amplitude signals due to physiological artifacts, such as breathing, muscle, and movement artifacts, making it difficult to determine the end of PGES. In this paper, we present a hybrid approach that combines the benefits of unsupervised and supervised learning for PGES detection using multi-channel EEG recordings. A K-means clustering model is leveraged to group EEG recordings with similar artifact features. We introduce a new learning strategy for training a set of random forest (RF) models based on clustering results to improve PGES detection performance. Our approach achieved a 5-second tolerance-based detection accuracy of 64.92%, a 10-second tolerance-based detection accuracy of 79.85%, and an average predicted time distance of 8.26 seconds with 286 EEG recordings using leave-one-out (LOO) cross-validation. The results demonstrated that our hybrid approach provided better performance compared to other existing approaches.

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