QRS detection in single-lead, telehealth electrocardiogram signals: Benchmarking open-source algorithms.

ABSTRACT:

Background and objectives

A key step in electrocardiogram (ECG) analysis is the detection of QRS complexes, particularly for arrhythmia detection. Telehealth ECGs present a new challenge for automated analysis as they are noisier than traditional clinical ECGs. The aim of this study was to identify the best-performing open-source QRS detector for use with telehealth ECGs.

Methods

The performance of 18 open-source QRS detectors was assessed on six datasets. These included four datasets of ECGs collected under supervision, and two datasets of telehealth ECGs collected without clinical supervision. The telehealth ECGs, consisting of single-lead ECGs recorded between the hands, included a novel dataset of 479 ECGs collected in the SAFER study of screening for atrial fibrillation

SUBMITTER: Kristof F 

PROVIDER: S-EPMC7617317 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature