Assessment of the atrial fibrillation burden in Holter electrocardiogram recordings using artificial intelligence.

ABSTRACT:

Background

Emerging evidence indicates that a high atrial fibrillation (AF) burden is associated with adverse outcome. However, AF burden is not routinely measured in clinical practice. An artificial intelligence (AI)-based tool could facilitate the assessment of AF burden.

Objective

We aimed to compare the assessment of AF burden performed manually by physicians with that measured by an AI-based tool.

Methods

We analyzed 7-day Holter electrocardiogram (ECG) recordings of AF patients included in the prospective, multicenter Swiss-AF Burden cohort study. AF burden was defined as percentage of time in AF, and was assessed manually by physicians and by an AI-based tool (Cardiomatics, Cracow, Poland). We evaluated the agreement between both techniques by means of Pearson

SUBMITTER: Hennings E 

PROVIDER: S-EPMC10123500 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature