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

ABSTRACT: 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. Aft

SUBMITTER: Villa A 

PROVIDER: S-EPMC9043208 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature