Functional feature extraction and validation from twelve-lead electrocardiograms to identify atrial fibrillation.

ABSTRACT:

Background

Deep learning methods on standard, 12-lead electrocardiograms (ECG) have resulted in the ability to identify individuals at high-risk for the development of atrial fibrillation. However, the process remains a "black box" and does not help clinicians in understanding the electrocardiographic changes at an individual level. we propose a nonparametric feature extraction approach to identify features that are associated with the development of atrial fibrillation (AF).

Methods

We apply functional principal component analysis to the raw ECG tracings collected in the Chronic Renal Insufficiency Cohort (CRIC) study. We define and select the features using ECGs from participants enrolled in Phase I (2003-2008) of the study. Cox proportional hazards models are used to eval

SUBMITTER: Yang W 

PROVIDER: S-EPMC11788424 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature