Temporal Heart Rhythm Clusters and Physiomorphic Age Mapping: A Deep Learning Approach to Cardiovascular Risk Stratification

Abstract

AbstractPurposeUnderstanding the intricate relationships between sleep quality and cardiovascular outcomes can potentially offer new avenues in risk stratification for cardiovascular diseases (CVD). This study aimed to evaluate the significance of biological age predicted through the analysis of sleep stages and nocturnal heart rhythms as a marker for cardiovascular risk.MethodsWe leveraged an unsupervised learning approach to generate time-series clusters utilizing whole-night sleep data fromN= 900 patients, focusing on identifying shifts and consistencies in nocturnal heart rhythms that may indicate variations in cardiac health. Following this, a deep learning model was applied to the time-series clusters to estimate the biological age of the individuals, thereby delineating potential relationships between predicted age, biological age, sleep patterns, and heart rhythms.ResultsIn a distinct test set of 736 individuals, the predicted age based on this experiment showcased a higher association with mortality (Hazard Ratio (HR) 2.27, p<0.05) and CVD risk (HR 3.56, p<0.001). Conversely, the age estimated through only nocturnal heart rhythms demonstrated a HR of 2.29 (p<0.05) for all-cause mortality and 3.13 (p<0.01) for CVD risk.ConclusionOur findings underscore the high prognostic potential of sleep and electrocardiography data in predicting cardiovascular risks. The method of utilizing predicted biological age derived from sleep stages and nocturnal heart rhythms stands as a significant metric in risk stratification for CVD. Further research in this area might foster novel strategies for early interventions based on sleep quality and cardiac health markers, potentially saving numerous lives through early detection and intervention.Author summaryThis study conducted on a large database of sleep data containing physiological signals such as Electrocardiograms, Sleep Stages, anonymized patient information among others shows that the heart behaviour during sleep is indicative of future cardiovascular (CVD) risk and all-cause mortality. This study employs deep learning to predict biological age which is in turn mapped to CVD risk. Through this study, we can see that while heart rhythms during sleep and different stages of sleep (REM, light sleep, etc) does show an association with CVD risk (this exists in previous literature), the more reliable association is found in heart behaviour during specific sleep stages (which is the novelty of our work). We use deep learning to map ECG into different clusters (n=50) using self-supervised learning, and also to find correlation between these clusters and sleep stages while mapping them to their biological age.