Information Dynamics of the Heart and Respiration Rates: a Novel Venue for Digital Phenotyping in Humans

In recent decade, wearable digital devices have shown potentials for the discovery of novel biomarkers of humans physiology and behavior. Heart rate (HR) and respiration rate (RR) are most crucial bio-signals in humans digital phenotyping research. HR is a continuous and non-invasive proxy to autonomic nervous system and ample evidence pinpoints the critical role of respiratory modulation of cardiac function. In the present study, we recorded longitudinal (up to 6 days, 4.63 {+/-} 1.52) HR and RR of 89 freely-behaving human subjects (Female: 39, age 57.28 {+/-} 5.67, Male: 50, age 58.48 {+/-} 6.32) and analyzed their HR and RR dynamics using linear models and information theoretic measures. While the predictability by linear autoregressive (AR) showed correlation with subjects age, an information theoretic measure of predictability, active information storage (AIS), captured these correlations more clearly. Furthermore, analysis of the information flow between HR and RR by transfer entropy (i.e., HR [->] RR and RR [->] HR) revealed that RR [->] HR is correlated with alcohol consumption and exercise habits. Thus we propose the AIS of HR and the transfer entropy RR [->] HR as two-dimensional biomarkers of cardiorespiratory physiology for digital phenotyping. The present findings provided evidence for the critical role of the respiratory modulation of HR, which was previously only studied in non-human animals.