Atrial fibrillation detection in outpatient electrocardiogram monitoring: An algorithmic crowdsourcing approach.

ABSTRACT:

Background

Atrial fibrillation (AFib) is the most common cardiac arrhythmia associated with stroke, blood clots, heart failure, coronary artery disease, and/or death. Multiple methods have been proposed for AFib detection, with varying performances, but no single approach appears to be optimal. We hypothesized that each state-of-the-art algorithm is appropriate for different subsets of patients and provides some independent information. Therefore, a set of suitably chosen algorithms, combined in a weighted voting framework, will provide a superior performance to any single algorithm.

Methods

We investigate and modify 38 state-of-the-art AFib classification algorithms for a single-lead ambulatory electrocardiogram (ECG) monitoring device. All algorithms are ranked using a ran

SUBMITTER: Bahrami Rad A 

PROVIDER: S-EPMC8594842 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature