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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14110%2F22%3A00125981" target="_blank" >RIV/00216224:14110/22:00125981 - isvavai.cz</a>

Result on the web

<a href="https://www.nature.com/articles/s41598-022-10452-0" target="_blank" >https://www.nature.com/articles/s41598-022-10452-0</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/s41598-022-10452-0" target="_blank" >10.1038/s41598-022-10452-0</a>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

30201 - Cardiac and Cardiovascular systems

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Nature Scientific Reports

ISSN

2045-2322

e-ISSN

—

Volume of the periodical

12

Issue of the periodical within the volume

1

Country of publishing house

DE - GERMANY

Number of pages

15

Pages from-to

1-15

UT code for WoS article

000787775900072

EID of the result in the Scopus database

2-s2.0-85128970841