Temporal Variability and Influence of Measurement Conditions of AI‐Based Atrial Fibrillation Risk Estimation

TL;DR

This study examines how AI-based atrial fibrillation risk estimation varies over time and with changes in ECG recording conditions.

Contribution

The study introduces a detailed evaluation of temporal variability and electrode placement effects on AI-based AF risk estimation.

Findings

AF risk estimation showed 95% reproducibility within 4 minutes and 87% over 15 minutes.

Shifting precordial electrodes or moving limb electrodes to the torso caused significant changes in AF risk levels.

Increased brain natriuretic peptide levels were associated with higher variability in AI-based risk estimation.

Abstract

Although artificial intelligence (AI) has been developed to identify patients with paroxysmal atrial fibrillation (PAF) during sinus rhythm, information on its variability remains limited. We evaluated the reproducibility and effect of recording condition on the estimation of AF risk using an electrocardiography (ECG) machine equipped with an AI‐based program.

We extracted two ECG data from a single ECG test in 149 patients to evaluate reproducibility within 4 min. We also recorded ECG signals under 12 conditions (standard, two conditions shifting precordial electrodes, five conditions moving limb electrodes to the torso, three conditions contaminating noise, and reproducibility over 15 min) in 30 participants to evaluate changes from the standard. The results of the AF risk estimation are expressed at four levels.

The rate of participants within one level of error was 95% for reproducibility within 4 min and 87% for reproducibility over 15 min. Shifting the precordial electrodes upward or downward and replacing the left leg electrode with the torso electrode frequently caused a two‐ or three‐level change. In clinical information, increased brain natriuretic peptide tended to increase the variability.

The AF risk estimated by the AI‐based program exhibited temporal variability. Shifting the precordial electrodes influenced AI‐based AF risk estimation.

The atrial fibrillation (AF) risk estimated by artificial intelligence‐based program installed in electrocardiography (ECG) machine exhibited temporal variability. Reproducibility within one level of error was 100% for the same ECG, 95% for reproducibility within 4 min interval, and 87% for reproducibility over 15 min interval.

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