Machine Learning Using a Single-Lead ECG to Identify Patients With Atrial Fibrillation-Induced Heart Failure

• Published in: Frontiers in cardiovascular medicine Vol. 9; p. 812719
• Main Authors: Luongo, Giorgio, Rees, Felix, Nairn, Deborah, Rivolta, Massimo W, Dössel, Olaf, Sassi, Roberto, Ahlgrim, Christoph, Mayer, Louisa, Neumann, Franz-Josef, Arentz, Thomas, Jadidi, Amir, Loewe, Axel, Müller-Edenborn, Björn
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 28.02.2022
• Subjects: atrial fibrillation; Cardiovascular Medicine; diagnostic tool; ECG; heart failure; machine learning; RR intervals; ECG; heart failure; atrial fibrillation; machine learning; RR intervals; diagnostic tool
• ISSN: 2297-055X; 2297-055X
• DOI: 10.3389/fcvm.2022.812719

Atrial fibrillation (AF) and heart failure often co-exist. Early identification of AF patients at risk for AF-induced heart failure (AF-HF) is desirable to reduce both morbidity and mortality as well as health care costs. We aimed to leverage the characteristics of beat-to-beat-patterns in AF to prospectively discriminate AF patients with and without AF-HF. A dataset of 10,234 5-min length RR-interval time series derived from 26 AF-HF patients and 26 control patients was extracted from single-lead Holter-ECGs. A total of 14 features were extracted, and the most informative features were selected. Then, a decision tree classifier with 5-fold cross-validation was trained, validated, and tested on the dataset randomly split. The derived algorithm was then tested on 2,261 5-min segments from six AF-HF and six control patients and validated for various time segments. The algorithm based on the spectral entropy of the RR-intervals, the mean value of the relative RR-interval, and the root mean square of successive differences of the relative RR-interval yielded an accuracy of 73.5%, specificity of 91.4%, sensitivity of 64.7%, and PPV of 87.0% to correctly stratify segments to AF-HF. Considering the majority vote of the segments of each patient, 10/12 patients (83.33%) were correctly classified. Beat-to-beat-analysis using a machine learning classifier identifies patients with AF-induced heart failure with clinically relevant diagnostic properties. Application of this algorithm in routine care may improve early identification of patients at risk for AF-induced cardiomyopathy and improve the yield of targeted clinical follow-up.