Continuous Heart Rate Recovery Monitoring with ECG Signals from Wearables: Identifying Risk Groups in the General Population.

Abstract

Heart rate recovery (HRR) is a critical indicator of cardiovascular fitness and autonomic nervous system function, reflecting the balance between sympathetic and parasympathetic activity. Slower HRR is often linked to cardiovascular and metabolic disorders, highlighting its potential for identifying high-risk individuals. In this study, we developed a feature engineering approach integrated to wearable device data to classify individuals into high-risk (slower HRR) and low-risk (faster HRR) groups. Data were collected from 38 participants (aged 20 to 76 years, 55.26% women) during treadmill trial, with ECG signals recorded using a smart shirt. Participants with an HRR equal to 28 beats per minute or below were classified as high-risk. Using machine learning classifiers, our approach achieved an area under the curve (AUC) score of 86% with Support Vector Classifier (SVC), demonstrating the feasibility of continuous heart health monitoring via wearable devices. Interestingly, age did not emerge as a significant predictor of HRR in our analysis, possibly due to the impact of lifestyle changes during the lockdown policy of COVID-19 era. This method holds promise for improving cardiovascular health monitoring accessibility and could support physicians in risk assessment and clinical decision-making.