Seizure detection using heart rate variability: A prospective validation study

Epilepsia. 2020 Nov:61 Suppl 1:S41-S46. doi: 10.1111/epi.16511. Epub 2020 May 7.

Abstract

Although several validated seizure detection algorithms are available for convulsive seizures, detection of nonconvulsive seizures remains challenging. In this phase 2 study, we have validated a predefined seizure detection algorithm based on heart rate variability (HRV) using patient-specific cutoff values. The validation data set was independent from the previously published data set. Electrocardiography (ECG) was recorded using a wearable device (ePatch) in prospectively recruited patients. The diagnostic gold standard was inferred from video-EEG monitoring. Because HRV-based seizure detection is suitable only for patients with marked ictal autonomic changes, we defined responders as the patients who had a>50 beats/min ictal change in heart rate. Eleven of the 19 included patients with seizures (57.9%) fulfilled this criterion. In this group, the algorithm detected 20 of the 23 seizures (sensitivity: 87.0%). The algorithm detected all but one of the 10 recorded convulsive seizures and all of the 8 focal impaired awareness seizures, and it missed 2 of the 4 focal aware seizures. The median sensitivity per patient was 100% (in nine patients all seizures were detected). The false alarm rate was 0.9/24 h (0.22/night). Our results suggest that HRV-based seizure detection has high performance in patients with marked autonomic changes.

Keywords: convulsive seizures; electrocardiography; heart rate variability; nonconvulsive seizures; seizure detection; wearable devices.

Publication types

  • Clinical Trial, Phase II
  • Research Support, Non-U.S. Gov't
  • Validation Study

MeSH terms

  • Adolescent
  • Adult
  • Algorithms*
  • Child
  • Child, Preschool
  • Electrocardiography / instrumentation*
  • Female
  • Heart Rate / physiology*
  • Humans
  • Male
  • Middle Aged
  • Prospective Studies
  • Seizures / diagnosis*
  • Sensitivity and Specificity
  • Signal Processing, Computer-Assisted
  • Wearable Electronic Devices*
  • Young Adult