Publication detail

Ultra-High Frequency ECG Deep-Learning Beat Detector Delivering QRS Onsets and Offsets

KOŠČOVÁ, Z. SMÍŠEK, R. NEJEDLÝ, P. HALÁMEK, J. JURÁK, P. LEINVEBER, P. ČURILA, K. PLEŠINGER, F.

Original Title

Ultra-High Frequency ECG Deep-Learning Beat Detector Delivering QRS Onsets and Offsets

Type

conference paper

Language

English

Original Abstract

Background: QRS duration is a common measure linked to conduction abnormalities in heart ventricles. Aim: We propose a QRS detector, further able to locate QRS onset and offset in one inference step. Method: A 3-second window from 12 leads of UHF ECG signal (5 kHz) is standardized and processed with the UNet network. The output is an array of QRS probabilities, further processed with probability and distance criterion, allowing us to determine duration and final location of QRSs. Results: The model was trained on 2,250 ECG recordings from the FNUSA-ICRC hospital (Brno, Czechia). The model was tested on 5 different datasets: FNUSA, a dataset from FNKV hospital (Prague, Czechia), and three public datasets (Cipa, Strict LBBB, LUDB). Regarding QRS duration, results showed a mean absolute error of 13.99 ± 4.29 ms between annotated durations and the output of the proposed model. A QRS detection F-score was 0.98 ± 0.01. Conclusion: Our results indicate high QRS detection performance on both spontaneous and paced UHF ECG data. We also showed that QRS detection and duration could be combined in one deep learning algorithm.

Keywords

ECG

Authors

KOŠČOVÁ, Z.; SMÍŠEK, R.; NEJEDLÝ, P.; HALÁMEK, J.; JURÁK, P.; LEINVEBER, P.; ČURILA, K.; PLEŠINGER, F.

Released

10. 12. 2022

ISBN

2325-887X

Periodical

Computing in Cardiology

State

United States of America

Pages count

4

BibTex

@inproceedings{BUT179995,
  author="Zuzana {Koščová} and Radovan {Smíšek} and Petr {Nejedlý} and Josef {Halámek} and Pavel {Jurák} and Pavel {Leinveber} and Karol {Čurila} and Filip {Plešinger}",
  title="Ultra-High Frequency ECG Deep-Learning Beat Detector Delivering QRS Onsets and Offsets",
  booktitle="Computing in Cardiology 2022",
  year="2022",
  journal="Computing in Cardiology",
  pages="4",
  issn="2325-887X"
}