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.
Author details: | Jonas ChromikORCiD, Lukas PirlORCiD, Jossekin Jakob BeilharzORCiD, Bert ArnrichORCiDGND, Andreas PolzeORCiDGND |
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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): | CC-BY - Namensnennung 4.0 International |