Seizure detection using heart rate variability: A prospective validation study

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Seizure detection using heart rate variability: A prospective validation study. / Jeppesen, Jesper; Fuglsang-Frederiksen, Anders; Johansen, Peter; Christensen, Jakob; Wüstenhagen, Stephan; Tankisi, Hatice; Qerama, Erisela; Beniczky, Sándor.

I: Epilepsia, Bind 61 , Nr. S1, 11.2020, s. S41-S46.

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

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@article{0345f722eca04e11b0d4a33705ddef35,
title = "Seizure detection using heart rate variability:: A prospective validation study",
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, EPILEPTIC SEIZURES",
author = "Jesper Jeppesen and Anders Fuglsang-Frederiksen and Peter Johansen and Jakob Christensen and Stephan W{\"u}stenhagen and Hatice Tankisi and Erisela Qerama and S{\'a}ndor Beniczky",
year = "2020",
month = nov,
doi = "10.1111/epi.16511",
language = "English",
volume = "61 ",
pages = "S41--S46",
journal = "Epilepsia",
issn = "0013-9580",
publisher = "Wiley-Blackwell Publishing, Inc.",
number = "S1",

}

RIS

TY - JOUR

T1 - Seizure detection using heart rate variability:

T2 - A prospective validation study

AU - Jeppesen, Jesper

AU - Fuglsang-Frederiksen, Anders

AU - Johansen, Peter

AU - Christensen, Jakob

AU - Wüstenhagen, Stephan

AU - Tankisi, Hatice

AU - Qerama, Erisela

AU - Beniczky, Sándor

PY - 2020/11

Y1 - 2020/11

N2 - 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.

AB - 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.

KW - convulsive seizures

KW - electrocardiography

KW - heart rate variability

KW - nonconvulsive seizures

KW - seizure detection

KW - wearable devices

KW - EPILEPTIC SEIZURES

UR - http://www.scopus.com/inward/record.url?scp=85085139126&partnerID=8YFLogxK

U2 - 10.1111/epi.16511

DO - 10.1111/epi.16511

M3 - Journal article

C2 - 32378197

AN - SCOPUS:85085139126

VL - 61

SP - S41-S46

JO - Epilepsia

JF - Epilepsia

SN - 0013-9580

IS - S1

ER -