A machine learning algorithm using three beat-to-beat features from a single-lead ECG identified patients with atrial fibrillation-induced heart failure with an accuracy of 73.5%.
Observational (n=52)
Does a machine learning algorithm using single-lead ECG beat-to-beat patterns accurately identify patients with atrial fibrillation-induced heart failure?
A machine learning classifier using single-lead ECG RR-interval features can identify patients with atrial fibrillation-induced heart failure with high specificity.
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.
Luongo et al. (2022) conducted an observational in Atrial Fibrillation-Induced Heart Failure (n=52). Machine learning algorithm (decision tree classifier) using single-lead ECG vs. Control group (AF patients without heart failure) was evaluated on Accuracy of the algorithm to identify AF-HF patients from 5-min Holter ECG segments recorded during daytime. A machine learning algorithm using three beat-to-beat features from a single-lead ECG identified patients with atrial fibrillation-induced heart failure with an accuracy of 73.5%.