J 2022

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

VILLA, Amalia, Bert VANDENBERK, Tuomas KENTTA, Sebastian INGELAERE, Heikki V. HUIKURI et. al.

Basic information

Original name

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

Authors

VILLA, Amalia (guarantor), Bert VANDENBERK, Tuomas KENTTA, Sebastian INGELAERE, Heikki V. HUIKURI, Markus ZABEL, Tim FRIEDE, Christian STICHERLING, Anton TUINENBURG, Marek MALÍK (203 Czech Republic, belonging to the institution), Sabine VAN HUFFEL, Rik WILLEMS and Carolina VARON

Edition

Nature Scientific Reports, London, NATURE RESEARCH, 2022, 2045-2322

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

30201 Cardiac and Cardiovascular systems

Country of publisher

Germany

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

Impact factor

Impact factor: 4.600

RIV identification code

RIV/00216224:14110/22:00125981

Organization unit

Faculty of Medicine

DOI

UT WoS

000787775900072

Keywords in English

electrocardiographic fQRS quantification; machine learning algorithm

Tags

Tags

International impact, Reviewed
Změněno: 10/6/2022 14:37, Mgr. Tereza Miškechová

Abstract

V originále

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.