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@article{1859443,
author = {Villa, Amalia and Vandenberk, Bert and Kentta, Tuomas and Ingelaere, Sebastian and Huikuri, Heikki V. and Zabel, Markus and Friede, Tim and Sticherling, Christian and Tuinenburg, Anton and Malík, Marek and Van Huffel, Sabine and Willems, Rik and Varon, Carolina},
article_location = {London},
article_number = {1},
doi = {http://dx.doi.org/10.1038/s41598-022-10452-0},
keywords = {electrocardiographic fQRS quantification; machine learning algorithm},
language = {eng},
issn = {2045-2322},
journal = {Nature Scientific Reports},
title = {A machine learning algorithm for electrocardiographic fQRS quantification validated on multi-center data},
url = {https://www.nature.com/articles/s41598-022-10452-0},
volume = {12},
year = {2022}
}
TY - JOUR
ID - 1859443
AU - Villa, Amalia - Vandenberk, Bert - Kentta, Tuomas - Ingelaere, Sebastian - Huikuri, Heikki V. - Zabel, Markus - Friede, Tim - Sticherling, Christian - Tuinenburg, Anton - Malík, Marek - Van Huffel, Sabine - Willems, Rik - Varon, Carolina
PY - 2022
TI - A machine learning algorithm for electrocardiographic fQRS quantification validated on multi-center data
JF - Nature Scientific Reports
VL - 12
IS - 1
SP - 1-15
EP - 1-15
PB - NATURE RESEARCH
SN - 20452322
KW - electrocardiographic fQRS quantification
KW - machine learning algorithm
UR - https://www.nature.com/articles/s41598-022-10452-0
N2 - 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.
ER -
VILLA, Amalia, Bert VANDENBERK, Tuomas KENTTA, Sebastian INGELAERE, Heikki V. HUIKURI, Markus ZABEL, Tim FRIEDE, Christian STICHERLING, Anton TUINENBURG, Marek MALÍK, Sabine VAN HUFFEL, Rik WILLEMS and Carolina VARON. A machine learning algorithm for electrocardiographic fQRS quantification validated on multi-center data. \textit{Nature Scientific Reports}. London: NATURE RESEARCH, 2022, vol.~12, No~1, p.~1-15. ISSN~2045-2322. Available from: https://dx.doi.org/10.1038/s41598-022-10452-0.
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