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Villa, A., Vandenberk, B., Kenttä, T. et al. A machine learning algorithm for electrocardiographic fQRS quantification validated on multi-center data. Sci Rep 12, 6783 (2022).

A machine learning algorithm for electrocardiographic fQRS quantification validated on multi-center data

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Author: Villa, Amalia1; Vandenberk, Bert2,3; Kenttä, Tuomas4;
Organizations: 1Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
2Department of Cardiovascular Diseases, Experimental Cardiology, KU Leuven, Leuven, Belgium
3Department of Cardiac Sciences, Libin Cardiovascular Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
4Research Unit of Internal Medicine, Medical Research Center Oulu, University of Oulu and Oulu University Hospital, Oulu, Finland
5Department of Cardiology and Pneumology, Heart Center, University of Göttingen Medical Center, Göttingen, Germany
6Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany
7DZHK (German Center of Cardiovascular Research), partner site Göttingen, Göttingen, Germany
8Division of Cardiology, University of Basel Hospital, Basel, Switzerland
9Department of Cardiology, University Medical Center Utrecht, Utrecht, Netherlands
10National Heart and Lung Institute, Imperial College, London, UK
11Department of Internal Medicine and Cardiology, Masaryk University, Brno, Czech Republic
12Microgravity Research Center, Université Libre de Bruxelles, Brussels, Belgium
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 4 MB)
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Language: English
Published: Springer Nature, 2022
Publish Date: 2022-10-25


Fragmented QRS (fQRS) is an electrocardiographic (ECG) marker of myocardial conduction abnormality, characterized by additional notches in the QRS complex. The presence of fQRS has been associated with an increased risk of all-cause mortality and arrhythmia in patients with cardiovascular disease. However, current binary visual analysis is prone to intra- and inter-observer variability and different definitions are problematic in clinical practice. Therefore, objective quantification of fQRS is needed and could further improve risk stratification of these patients. We present an automated method for fQRS detection and quantification. First, a novel robust QRS complex segmentation strategy is proposed, which combines multi-lead information and excludes abnormal heartbeats automatically. Afterwards extracted features, based on variational mode decomposition (VMD), phase-rectified signal averaging (PRSA) and the number of baseline-crossings of the ECG, were used to train a machine learning classifier (Support Vector Machine) to discriminate fragmented from non-fragmented ECG-traces using multi-center data and combining different fQRS criteria used in clinical settings. The best model was trained on the combination of two independent previously annotated datasets and, compared to these visual fQRS annotations, achieved Kappa scores of 0.68 and 0.44, respectively. We also show that the algorithm might be used in both regular sinus rhythm and irregular beats during atrial fibrillation. These results demonstrate that the proposed approach could be relevant for clinical practice by objectively assessing and quantifying fQRS. The study sets the path for further clinical application of the developed automated fQRS algorithm.

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Series: Scientific reports
ISSN: 2045-2322
ISSN-E: 2045-2322
ISSN-L: 2045-2322
Volume: 12
Issue: 1
Article number: 6783
DOI: 10.1038/s41598-022-10452-0
Type of Publication: A1 Journal article – refereed
Field of Science: 3121 General medicine, internal medicine and other clinical medicine
Funding: BV is supported by a research grant of the Frans Van de Werf Fund for Clinical Cardiovascular Research. RW is supported as postdoctoral clinical researcher by the Fund for Scientific Research Flanders (FWO Vlaanderen). SVH and AV received funding from the Flemish Government (AI Research Program) and are affiliated to Leuven.AI - KU Leuven institute for AI, B-3000, Leuven, Belgium. EU-CERT-ICD is funded by the European Commission within the 7th Framework Programme under Grant Agreement n∘602299. CV acknowledges the financial support of the European Space Agency (ESA), BELSPO - NEPTUNE.
EU Grant Number: (602299) EU-CERT-ICD - Comparative Effectiveness Research to Assess the Use of Primary ProphylacTic Implantable Cardioverter Defibrillators in EUrope
Dataset Reference: The online version contains supplementary material available at 10.1038/s41598-022-10452-0. 10.1038/s41598-022-10452-0
Copyright information: © The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit