Work

AI-based prediction of heart failure progression in persistent atrial fibrillation using wearable electrocardiography: a brief research report

Year: 2026

Type: article

Abstract: Background: Persistent atrial fibrillation (AF) frequently coexists with heart failure (HF), yet HF monitoring remains limited by the need for repeated blood-based biomarkers such as N-terminal pro-brain natriuretic peptide (NT-proBNP). Advances in wearable electrocardiography (ECG) and artificial intelligence (AI) now allow continuous extraction of digital physiologic signatures that may reflect hemodynamic stress. Objective: To evaluate the feasibility of predicting HF progression using wearable ECG-derived features in patients with persistent AF. Methods: Fifty patients with persistent AF underwent 3-7 days of single-lead ECG monitoring. Heart rate variability (HRV) and RR-interval features from 30 min windows were combined with baseline clinical metrics. A context-aware deep learning model using long short-term memory (LSTM) and attention mechanisms was trained to predict 6-12-month NT-proBNP changes. Model performance was assessed using root mean squared error (RMSE), mean absolute error (MAE), and the accuracy of directional NT-proBNP change. Results: The best performance was achieved when clinical metrics, RR features, and long-term HRV summaries were combined (RMSE 1,667.04; MAE 950.52). Directional classification of NT-proBNP trajectories achieved an accuracy of 0.82. ECG-only models performed comparably to multimodal models. Conclusion: Wearable ECG-based AI modeling is feasible for predicting trends in HF biomarkers in persistent AF. These results provide early evidence that ECG-derived digital biomarkers may offer a scalable, non-invasive approach for longitudinal HF monitoring.

Source: Frontiers in Cardiovascular Medicine

DOI: 10.3389/fcvm.2025.1748673

Authors: Chae-Bin Song, Yun Kwan Kim, YouMi Hwang

Institutions: Korea University, The Catholic University of Korea St. Vincent's Hospital, Catholic University of Korea

Language: English

Topic: ECG Monitoring and Analysis

Subfield: Cardiology and Cardiovascular Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,801