Frontiers in Neurology (Mar 2022)

Interictal EEG and ECG for SUDEP Risk Assessment: A Retrospective Multicenter Cohort Study

  • Zhe Sage Chen,
  • Zhe Sage Chen,
  • Aaron Hsieh,
  • Guanghao Sun,
  • Gregory K. Bergey,
  • Samuel F. Berkovic,
  • Samuel F. Berkovic,
  • Piero Perucca,
  • Piero Perucca,
  • Piero Perucca,
  • Piero Perucca,
  • Piero Perucca,
  • Wendyl D'Souza,
  • Christopher J. Elder,
  • Pue Farooque,
  • Emily L. Johnson,
  • Sarah Barnard,
  • Sarah Barnard,
  • Sarah Barnard,
  • Russell Nightscales,
  • Russell Nightscales,
  • Russell Nightscales,
  • Russell Nightscales,
  • Patrick Kwan,
  • Patrick Kwan,
  • Patrick Kwan,
  • Patrick Kwan,
  • Brian Moseley,
  • Terence J. O'Brien,
  • Terence J. O'Brien,
  • Terence J. O'Brien,
  • Terence J. O'Brien,
  • Shobi Sivathamboo,
  • Shobi Sivathamboo,
  • Shobi Sivathamboo,
  • Shobi Sivathamboo,
  • Juliana Laze,
  • Daniel Friedman,
  • Daniel Friedman,
  • Orrin Devinsky,
  • Orrin Devinsky,
  • Orrin Devinsky,
  • The MS-BioS Study Group

DOI
https://doi.org/10.3389/fneur.2022.858333
Journal volume & issue
Vol. 13

Abstract

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ObjectiveSudden 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 postictal 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.MethodsThis 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 cross-validated classification accuracy and the area under the receiver operating characteristic (AUC) curve.ResultsThe 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 patients with SUDEP [14 females; mean age (SD), 31 (8.47) years] and 58 living epilepsy controls [26 females (43%); mean age (SD) 31 (8.5) years], the LR model achieved the median AUC of 0.77 [interquartile range (IQR), 0.73–0.80] in five-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 the mean AUC of 0.79 in leave-one-center-out prediction.ConclusionsOur 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 to identify high-risk patients and initiate preventive strategies.

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