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Certainty in QRS detection with artificial neural networks

  • Detection of the QRS complex is a long-standing topic in the context of electrocardiography and many algorithms build upon the knowledge of the QRS positions. Although the first solutions to this problem were proposed in the 1970s and 1980s, there is still potential for improvements. Advancements in neural network technology made in recent years also lead to the emergence of enhanced QRS detectors based on artificial neural networks. In this work, we propose a method for assessing the certainty that is in each of the detected QRS complexes, i.e. how confident the QRS detector is that there is, in fact, a QRS complex in the position where it was detected. We further show how this metric can be utilised to distinguish correctly detected QRS complexes from false detections.

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Author details:Jonas ChromikORCiD, Lukas PirlORCiD, Jossekin Jakob BeilharzORCiD, Bert ArnrichORCiDGND, Andreas PolzeORCiDGND
DOI:https://doi.org/10.1016/j.bspc.2021.102628
ISSN:1746-8094
ISSN:1746-8108
Title of parent work (English):Biomedical signal processing and control
Publisher:Elsevier
Place of publishing:Oxford
Publication type:Article
Language:English
Date of first publication:2021/04/24
Publication year:2021
Release date:2023/01/02
Tag:Artificial neural networks; Electrocardiography; Machine; QRS detection; Signal-to-noise ratio; learning
Volume:68
Article number:102628
Number of pages:12
Funding institution:German Federal Ministry of Education and Research (BMBF)Federal Ministry of Education & Research (BMBF) [16SV8559]
Organizational units:An-Institute / Hasso-Plattner-Institut für Digital Engineering gGmbH
DDC classification:6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Peer review:Referiert
Publishing method:Open Access / Hybrid Open-Access
License (German):License LogoCC-BY - Namensnennung 4.0 International
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