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

Frontiers in Cardiovascular Medicine

article

Giorgio Luongo¹  Felix Rees²  Deborah Nairn¹  Massimo W. Rivolta³  Olaf Dössel¹  Roberto Sassi³  Christoph Ahlgrim²  Louisa Mayer²  Franz-Josef Neumann²  Thomas Arentz²  Amir Jadidi²  Axel Loewe¹  Björn Müller-Edenborn² 

[1] Institute of Biomedical Engineering ,(IBT), Karlsruhe Institute of Technology;Division of Cardiology and Angiology II, University Heart Center Freiburg-Bad Krozingen;Dipartimento di Informatica, Università degli Studi di Milano

关键词: atrial fibrillation;    heart failure;    machine learning;    ECG;    RR intervals;    diagnostic tool;   

DOI  :  10.3389/fcvm.2022.812719

学科分类：地球科学(综合)

来源: Frontiers

【 摘 要 】

Aims 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. Methods 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. Results 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. Conclusion 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.

【 授权许可】

CC BY