Frontiers in Neurology (Mar 2023)

Ictal ECG-based assessment of sudden unexpected death in epilepsy

  • Adam C. Gravitis,
  • Uilki Tufa,
  • Katherine Zukotynski,
  • Katherine Zukotynski,
  • David L. Streiner,
  • Daniel Friedman,
  • Juliana Laze,
  • Yotin Chinvarun,
  • Orrin Devinsky,
  • Richard Wennberg,
  • Peter L. Carlen,
  • Peter L. Carlen,
  • Berj L. Bardakjian,
  • Berj L. Bardakjian

DOI
https://doi.org/10.3389/fneur.2023.1147576
Journal volume & issue
Vol. 14

Abstract

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IntroductionPrevious case-control studies of sudden unexpected death in epilepsy (SUDEP) patients failed to identify ECG features (peri-ictal heart rate, heart rate variability, corrected QT interval, postictal heart rate recovery, and cardiac rhythm) predictive of SUDEP risk. This implied a need to derive novel metrics to assess SUDEP risk from ECG.MethodsWe applied Single Spectrum Analysis and Independent Component Analysis (SSA-ICA) to remove artifact from ECG recordings. Then cross-frequency phase-phase coupling (PPC) was applied to a 20-s mid-seizure window and a contour of −3 dB coupling strength was determined. The contour centroid polar coordinates, amplitude (alpha) and angle (theta), were calculated. Association of alpha and theta with SUDEP was assessed and a logistic classifier for alpha was constructed.ResultsAlpha was higher in SUDEP patients, compared to non-SUDEP patients (p < 0.001). Theta showed no significant difference between patient populations. The receiver operating characteristic (ROC) of a logistic classifier for alpha resulted in an area under the ROC curve (AUC) of 94% and correctly classified two test SUDEP patients.DiscussionThis study develops a novel metric alpha, which highlights non-linear interactions between two rhythms in the ECG, and is predictive of SUDEP risk.

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