Electrocardiogram sampling frequency for the optimal performance of complexity analysis and machine learning models: Discrimination between patients with and without paroxysmal atrial fibrillation using sinus rhythm electrocardiograms.

ABSTRACT:

Background

The current clinical practice to diagnose atrial fibrillation (AF) requires repeated episodic monitoring and significantly underperform in their ability to detect AF episodes.

Objective

There is therefore potential for artificial intelligence-based methods to assist in the detection of AF. Better understanding of the optimal parameters for this detection can potentially improve the sensitivity for detecting AF.

Methods

Ten-second, 12-lead electrocardiogram signals were analyzed using complexity algorithms combined with machine learning techniques to predict patients who had a previously detected AF episode but had since returned to normal sinus rhythm. An investigation was performed into the impact of the sampling frequency of the electrocardiogram signal o

SUBMITTER: Creasy S 

PROVIDER: S-EPMC11993800 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature