Key result
Wearable ECG random forest classifier predicts NYHA class in hospitalized HF patients with 0.85 AUC.
Why the study?
Wearable ECG monitoring faces challenges such as lower accuracy and susceptibility to motion artifacts and environmental noise, requiring signal processing strategies to enhance signal quality for heart failure assessment.
Does wearable ECG monitoring with a random forest classifier accurately predict NYHA functional classification in hospitalized heart failure patients?
Cohort
No
Does wearable ECG monitoring with a random forest classifier accurately predict NYHA functional classification in hospitalized heart failure patients?
A machine learning model using wearable ECG data demonstrated robust accuracy (AUC 0.85) in predicting NYHA functional class in hospitalized heart failure patients, highlighting its potential for remote monitoring.
May support remote NYHA monitoring in HF; hypothesis-generating and requires prospective validation.
The swift progress in wearable device technology has permitted electrocardiogram (ECG) monitoring to be conducted beyond the confines of traditional hospital settings, offering heart failure (HF) patients the benefit of affordable and prolonged home monitoring. This study addresses the challenges inherent in wearable ECG monitoring, including lower accuracy and increased susceptibility to motion artifacts and environmental noise, by employing a series of signal processing strategies to enhance ECG signal quality. We prospectively collected ECG signals from a cohort of hospitalized HF patients at West China Hospital using wearable devices, preprocessed the data to extract high-quality RR intervals, and developed a model using a random forest classifier combined with recursive feature elimination (RFE). The model manifested robust predictive accuracy, achieving an average AUC of 0.85 for both Micro- and Macro-Average ROC across New York Heart Association (NYHA) functional classifications. This research indicates the potential of wearable ECG monitoring in condition assessment and disease management for HF patients, pointing towards promising avenues for the future of personalized medicine and remote health monitoring strategies.
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Ying et al. (2024) conducted a cohort in Heart failure. Wearable ECG monitoring with random forest classifier was evaluated on Predictive accuracy across NYHA functional classifications (AUC 0.85). A random forest classifier using wearable ECG monitoring data achieved an average AUC of 0.85 for predicting NYHA functional classifications in hospitalized heart failure patients.
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