Interictal EEG and ECG for SUDEP Risk Assessment: A Retrospective Multicenter Cohort Study - Chen, Zhe Sage; Hsieh, Aaron; Sun, Guanghao; Bergey, Gregory K.; Berkovic, Samuel F.; Perucca, Piero; D'Souza, Wendyl; Elder, Christopher J.; Farooque, Pue; Johnson, Emily L.; Barnard, Sarah; Nightscales, Russell; et al. (Frontiers in Neurology, 2022)
Objective: Sudden unexpected death in epilepsy (SUDEP) is the leading cause of epilepsy-related mortality. Although lots of effort has been made in identifying clinical risk factors for SUDEP in the literature, there are few validated methods to predict individual SUDEP risk. Prolonged post-ictal EEG suppression (PGES) is a potential SUDEP biomarker, but its occurrence is infrequent and requires epilepsy monitoring unit admission. We use machine learning methods to examine SUDEP risk using interictal EEG and ECG recordings from SUDEP cases and matched living epilepsy controls. Methods. This multicenter, retrospective, cohort study examined interictal EEG and ECG recordings from 30 SUDEP cases and 58 age-matched living epilepsy patient controls. We trained machine learning models with interictal EEG and ECG features to predict the retrospective SUDEP risk for each patient. We assessed ross-validated classification accuracy and AUC (area under receiver operating characteristic curve). Results: The logistic regression (LR) classifier produced the overall best performance, outperforming the support vector machine (SVM), random forest (RF), and convolutional neural network (CNN). Among the 30 SUDEP patients (14 female; mean age [SD], 31 [8.47] years) and 58 living epilepsy controls (26 female [43%]; mean age [SD] 31 [8.5] years), the LR model achieved a median AUC of 0.77 (interquartile range [IQR], 0.73-0.80) in 5-fold cross-validation using interictal alpha and low gamma power ratio of the EEG and heart rate variability (HRV) features extracted from the ECG. The LR model achieved a mean AUC of 0.79 in leave-one-center-out prediction. Conclusions: Our results support that machine learning-driven models may quantify SUDEP risk for epilepsy patients, future refinements in our model may help predict individualized SUDEP risk and help clinicians correlate predictive scores with the clinical data. Low-cost and noninvasive interictal biomarkers of SUDEP risk may help clinicians identify high-risk patients and initiate preventive strategies.