Modified automatic R-peak detection algorithm for patients with epilepsy using a portable electrocardiogram recorder

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Modified automatic R-peak detection algorithm for patients with epilepsy using a portable electrocardiogram recorder. / Jeppesen, J; Beniczky, S; Fuglsang Frederiksen, A; Sidenius, P; Johansen, P.

I: I E E E Engineering in Medicine and Biology Society. Conference Proceedings, Bind 2017, 07.2017, s. 4082-4085.

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

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Jeppesen, J, Beniczky, S, Fuglsang Frederiksen, A, Sidenius, P & Johansen, P 2017, 'Modified automatic R-peak detection algorithm for patients with epilepsy using a portable electrocardiogram recorder', I E E E Engineering in Medicine and Biology Society. Conference Proceedings, bind 2017, s. 4082-4085. https://doi.org/10.1109/EMBC.2017.8037753

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Jeppesen, J ; Beniczky, S ; Fuglsang Frederiksen, A ; Sidenius, P ; Johansen, P. / Modified automatic R-peak detection algorithm for patients with epilepsy using a portable electrocardiogram recorder. I: I E E E Engineering in Medicine and Biology Society. Conference Proceedings. 2017 ; Bind 2017. s. 4082-4085.

Bibtex

@article{a96191ffb75940d59503aff233f2f9e6,
title = "Modified automatic R-peak detection algorithm for patients with epilepsy using a portable electrocardiogram recorder",
abstract = "Earlier studies have shown that short term heart rate variability (HRV) analysis of ECG seems promising for detection of epileptic seizures. A precise and accurate automatic R-peak detection algorithm is a necessity in a real-time, continuous measurement of HRV, in a portable ECG device. We used the portable CE marked ePatch{\circledR} heart monitor to record the ECG of 14 patients, who were enrolled in the videoEEG long term monitoring unit for clinical workup of epilepsy. Recordings of the first 7 patients were used as training set of data for the R-peak detection algorithm and the recordings of the last 7 patients (467.6 recording hours) were used to test the performance of the algorithm. We aimed to modify an existing QRS-detection algorithm to a more precise R-peak detection algorithm to avoid the possible jitter Qand S-peaks can create in the tachogram, which causes error in short-term HRVanalysis. The proposed R-peak detection algorithm showed a high sensitivity (Se = 99.979{\%}) and positive predictive value (P+ = 99.976{\%}), which was comparable with a previously published QRS-detection algorithm for the ePatch{\circledR} ECG device, when testing the same dataset. The novel R-peak detection algorithm designed to avoid jitter has very high sensitivity and specificity and thus is a suitable tool for a robust, fast, real-time HRV-analysis in patients with epilepsy, creating the possibility for real-time seizure detection for these patients.",
keywords = "Journal Article, Research Support, Non-U.S. Gov't",
author = "J Jeppesen and S Beniczky and {Fuglsang Frederiksen}, A and P Sidenius and P Johansen",
year = "2017",
month = "7",
doi = "10.1109/EMBC.2017.8037753",
language = "English",
volume = "2017",
pages = "4082--4085",
journal = "I E E E Engineering in Medicine and Biology Society. Conference Proceedings",
issn = "2375-7477",
publisher = "IEEE",

}

RIS

TY - JOUR

T1 - Modified automatic R-peak detection algorithm for patients with epilepsy using a portable electrocardiogram recorder

AU - Jeppesen, J

AU - Beniczky, S

AU - Fuglsang Frederiksen, A

AU - Sidenius, P

AU - Johansen, P

PY - 2017/7

Y1 - 2017/7

N2 - Earlier studies have shown that short term heart rate variability (HRV) analysis of ECG seems promising for detection of epileptic seizures. A precise and accurate automatic R-peak detection algorithm is a necessity in a real-time, continuous measurement of HRV, in a portable ECG device. We used the portable CE marked ePatch® heart monitor to record the ECG of 14 patients, who were enrolled in the videoEEG long term monitoring unit for clinical workup of epilepsy. Recordings of the first 7 patients were used as training set of data for the R-peak detection algorithm and the recordings of the last 7 patients (467.6 recording hours) were used to test the performance of the algorithm. We aimed to modify an existing QRS-detection algorithm to a more precise R-peak detection algorithm to avoid the possible jitter Qand S-peaks can create in the tachogram, which causes error in short-term HRVanalysis. The proposed R-peak detection algorithm showed a high sensitivity (Se = 99.979%) and positive predictive value (P+ = 99.976%), which was comparable with a previously published QRS-detection algorithm for the ePatch® ECG device, when testing the same dataset. The novel R-peak detection algorithm designed to avoid jitter has very high sensitivity and specificity and thus is a suitable tool for a robust, fast, real-time HRV-analysis in patients with epilepsy, creating the possibility for real-time seizure detection for these patients.

AB - Earlier studies have shown that short term heart rate variability (HRV) analysis of ECG seems promising for detection of epileptic seizures. A precise and accurate automatic R-peak detection algorithm is a necessity in a real-time, continuous measurement of HRV, in a portable ECG device. We used the portable CE marked ePatch® heart monitor to record the ECG of 14 patients, who were enrolled in the videoEEG long term monitoring unit for clinical workup of epilepsy. Recordings of the first 7 patients were used as training set of data for the R-peak detection algorithm and the recordings of the last 7 patients (467.6 recording hours) were used to test the performance of the algorithm. We aimed to modify an existing QRS-detection algorithm to a more precise R-peak detection algorithm to avoid the possible jitter Qand S-peaks can create in the tachogram, which causes error in short-term HRVanalysis. The proposed R-peak detection algorithm showed a high sensitivity (Se = 99.979%) and positive predictive value (P+ = 99.976%), which was comparable with a previously published QRS-detection algorithm for the ePatch® ECG device, when testing the same dataset. The novel R-peak detection algorithm designed to avoid jitter has very high sensitivity and specificity and thus is a suitable tool for a robust, fast, real-time HRV-analysis in patients with epilepsy, creating the possibility for real-time seizure detection for these patients.

KW - Journal Article

KW - Research Support, Non-U.S. Gov't

U2 - 10.1109/EMBC.2017.8037753

DO - 10.1109/EMBC.2017.8037753

M3 - Journal article

C2 - 29060794

VL - 2017

SP - 4082

EP - 4085

JO - I E E E Engineering in Medicine and Biology Society. Conference Proceedings

JF - I E E E Engineering in Medicine and Biology Society. Conference Proceedings

SN - 2375-7477

ER -