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

Autoři	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
Rok publikování	2022
Druh	Článek v odborném periodiku
Časopis / Zdroj	Nature Scientific Reports
Fakulta / Pracoviště MU	Lékařská fakulta
Citace
www	https://www.nature.com/articles/s41598-022-10452-0
Doi	http://dx.doi.org/10.1038/s41598-022-10452-0
Klíčová slova	electrocardiographic fQRS quantification; machine learning algorithm
Popis	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.