0555164 - ÚPT 2022 RIV US eng C - Conference Paper (international conference)
Plešinger, Filip - Ivora, Adam - Halámek, Josef - Viščor, Ivo - Smíšek, Radovan - Bulková, V. - Jurák, Pavel
Evaluating Pauses in Holter ECG Signals.
2021 Computing in Cardiology (CinC). Vol. 48. New York: IEEE, 2021, č. článku 107. ISBN 978-166547916-5. ISSN 2325-8861. E-ISSN 2325-887X.
[Computing in Cardiology 2021 /48./. Brno (CZ), 12.09.2021-15.09.2021]
R&D Projects: GA TA ČR(CZ) FW01010305
Institutional support: RVO:68081731
Keywords : Holter ECG * Evaluating Pauses
OECD category: Medical engineering
Result website:
https://ieeexplore.ieee.org/document/9662914DOI: https://doi.org/10.23919/CinC53138.2021.9662914

Background: Information related to pauses in heart activity is an important output of ECG Holter monitoring reports. This information should be quickly assessed from inter-beat (RR) intervals only (a naïve approach). However, evaluating pauses in Holter ECGs recorded during usual daily activities can be more challenging due to signal lower quality. In this paper, we propose a method to improve pause detection in heart activity from Holter ECG recordings. Method: We used 978 recordings (length 45 seconds, 1-lead ECG, sampled at 200 or 250 Hz) with a known longest RR interval (from 1.12 to 19.0 seconds, mean duration of 2.72 ± 1.26 seconds). QRS complexes were detected by a convolutional neural network with a recurrent layer. This study started with the automated removal of suspicious QRS complexes by a QRS amplitude. Then we iterated through RR intervals, seeking saturated areas, missed QRS, or a strong noise, potentially, examined RR intervals were further refined. The longest interval was reported for each recording. Results: The ability to find life-threatening pauses improved from an F1 score of 0.95 to 0.97. Conclusion: The presented method improved pause detection in Holter ECG recordings compared to the naïve approach.
Permanent Link: http://hdl.handle.net/11104/0329695