Wearable ECG Device Detects Nonconvulsive Seizures Successfully


New study results suggest that a wearable ECG device can predict nonconvulsive seizures with a sensitivity of 93.1% and all seizures with a sensitivity of 90.5%.

Dr Jesper Jeppesen

Jesper Jeppesen, PhD, department of clinical neurophysiology, Aarhus University

Jesper Jeppesen, PhD

In combination with an automated heart rate variability algorithm, a wearable electrocardiography (ECG) device can detect seizures, including nonconvulsive events, with high sensitivity, according to study results.1

This phase 2 analysis, conducted by Jesper Jeppesen, PhD, department of clinical neurophysiology, Aarhus University, and colleagues suggested that wearable ECG devices may be a feasible method of seizure detection in patients with epilepsy and seizure disorders. This study utilized FORCE Technology’s ePatch wearable ECG device.

Although many phase 2 and 3 studies have validated these devices in the detection of generalized tonic-clonic and focal to bilateral tonic-clonic seizures, Jeppesen et al. suggest that using this algorithm in tandem is a realistic method for individuals with prominent ictal autonomic changes.

“Detection of nonconvulsive seizures still needs further research, since currently available methods have either low sensitivity or an extremely high false alarm rate,” they wrote. The false alarm rate in this study was low during the night, measured at 0.11 per night, and 1.0 per 24 hours. “Typically, patients with prominent autonomic nervous system changes were responders.”

An ictal change of more than 50 heartbeats per minute demonstrated extrapolative value in determining responders with a positive predictive value of 87% and a negative predictive value of 90%.

The prospective study included 100 consecutive patients, of which 43 had 126 seizures—nonconvulsive (n = 108) and convulsive (n = 18)—which were included in analysis. The investigators compared 26 automated algorithms with seizure time-points marked by experts who reviewed the long-term video EEG monitoring that patients underwent. Response was determined by a seizure detection threshold of >66%.

All told, the top algorithm identified 53.5% of the cohort as responders, with a detection sensitivity of 93.1% (95% CI, 86.6—99.6) within the responding subgroup for all seizures and 90.5% (95% CI, 77.4–97.3) for nonconvulsive seizures. The median seizure detection latency was 30 seconds.

“The best‐performing heart rate variability algorithm combined a measure of sympathetic activity with a measure of how quickly heart rate changes occurred,” Jeppesen and colleagues described. This work builds upon prior work by Jeppesen and colleagues in 2015, in which they evaluated 4 heart rate variability methods to detect focal epileptic seizures in short-term gaps of 30, 50, and 100 R-R intervals.2

Those methods included reciprocal high-frequency power based on Fast Fourier Transformation, the Cardiac Sympathetic Index (CSI), a modified CSI both based on Lorenz plot, and a heart rate differential method. In that work, the modified CSI method at 100 R-R intervals was most accurate, detecting seizures for 13 of 17 patients within 6 prior and 50 seconds post-seizure onset.

Essentially all measures of this method are comparable to prior studies of wearable devices in the detection of convulsive seizures, including a 2018 study by Beniczky et al. in which generalized tonic-clonic seizures were detected with a sensitivity of 93.8% and a mean latency of 9 seconds. That work, published in Neurology, also showed a false alarm rate of 0.67 per 24 hours.3 Likewise, work from Regalia et al. assessing the Empatica wearable device has shown sensitivity ranging from 92% to 100% and a false alarm rate of 0.2 to 0.1 per 24 hours.4


1. Jeppesen J, Fuglsang-Frederiksen A, Johansen P, et al. Seizure detection based on heart rate variability using a wearable electrocardiography device. Epilepsia. 2019;60(10):2105-2113. doi: 10.1111/epi.16343.

2. Jeppesen J, Beniczky S, Johansen P, Sidenius P, Fuglsang-Frederiksen A. Detection of epileptic seizures with a modified heart rate variability algorithm based on Lorenz plot. Seizure. 2015;24:1-7. doi: 10.1016/j.seizure.2014.11.004.

3. Beniczky S, Conradsen I, Henning O, Fabricius M, Wolf P. Automated real-time detection of tonic-clonic seizures using a wearable EMG device. Neurology. 2018;90(5):e428-e434. doi: 10.1212/WNL.0000000000004893.

4. Regalia G, Onorati F, Lai M, Caborni C, Picard RW. Multimodal wrist-worn devices for seizure detection and advancing research: Focus on the Empatica wristbands. Epilepsy Res. 2019;153:79-82. doi: 10.1016/j.eplepsyres.2019.02.007.

Related Videos
Michael Levy, MD, PhD
Michael Kaplitt, MD, PhD
Michael Kaplitt, MD, PhD
video 4 - "Amyloid Cascade Hypothesis of Alzheimer’s Disease"
Video 3 - "Amyloid Precursor Protein and Amyloid Beta Species in Alzheimer’s Disease"
Svetlana Blitshteyn, MD, FAAN, director and founder of Dysautonomia Clinic
© 2024 MJH Life Sciences

All rights reserved.