Predicting Spontaneous Termination of Atrial Fibrillation Based on Analysis of Standard Electrocardiograms: A Systematic Review

• Published in: Annals of noninvasive electrocardiology 2024-11, Vol.29 (6), p.e70025-n/a
• Main Authors: Wadforth, Brandon, Goh, Jing Soong, Tiver, Kathryn, Shahrbabaki, Sobhan Salari, Tonchev, Ivaylo, Dharmaprani, Dhani, Ganesan, Anand N.
• Format: Article
• Language: English
• Subjects: Accuracy; atrial fibrillation; Atrial Fibrillation - diagnosis; Atrial Fibrillation - physiopathology; Cardiac arrhythmia; Cardioversion; Decision making; Economic impact; EKG; electrocardiogram; Electrocardiography; Emergency medical care; Emergency medical services; entropy; Fibrillation; Frequency analysis; Humans; Machine learning; Patients; prediction; Predictions; Predictive Value of Tests; Signal processing; Systematic review; termination; time–frequency analysis
• ISSN: 1082-720X; 1542-474X; 1542-474X
• DOI: 10.1111/anec.70025

ABSTRACT Background Forward prediction of atrial fibrillation (AF) termination is a challenging technical problem of increasing significance due to rising AF presentations to emergency departments worldwide. The ability to non‐invasively predict which AF episodes will terminate has important implications in terms of clinical decision‐making surrounding treatment and admission, with subsequent impacts on hospital capacity and the economic cost of AF hospitalizations. Methods and Results MEDLINE, EMCare, CINAHL, CENTRAL, and SCOPUS were searched on 29 July 2023 for articles where an attempt to predict AF termination was made using standard surface ECG recordings. The final review included 35 articles. Signal processing techniques fit into three broad categories including machine learning (n = 14), entropy analysis (n = 12), and time–frequency/frequency analysis (n = 9). Retrospectively processed ECG data was used in all studies with no prospective validation studies. Most studies (n = 33) utilized the same ECG database, which included recordings that either terminated within 1 min or continued for over 1 h. There was no significant difference in accuracy between groups (H(2) = 0.058, p‐value = 0.971). Only one study assessed recordings earlier than several minutes preceding termination, achieving 92% accuracy using the central 10 s of paroxysmal episodes lasting up to 174. Conclusions No studies attempted to forward predict AF termination in real‐time, representing an opportunity for novel prospective validation studies. Multiple signal processing techniques have proven accurate in predicting AF termination utilizing ECG recordings sourced from a database retrospectively. Three main approaches to predicting the spontaneous termination of atrial fibrillation using standard surface electrocardiograms included machine learning, entropy analysis, and time–frequency/frequency analysis. The median accuracy was 90% with no significant differences between groups (H(2) = 0.058, p‐value = 0.971). No studies validated their prediction models prospectively.