A Model-based Machine Learning Approach to Probing Autonomic Regulation from Nonstationary Vital-Signs Time Series.

Physiological variables, such as heart rate (HR), blood pressure (BP) and respiration (RESP), are tightly regulated and coupled under healthy conditions, and a break-down in the coupling has been associated with aging and disease. We present an approach that incorporates physiological modeling within a switching linear dynamical systems (SLDS) framework to assess the various functional components of the autonomic regulation through transfer function analysis of nonstationary multivariate time series of vital signs. We validate our proposed SLDS-based transfer function analysis technique in automatically capturing (i) changes in baroreflex gain due to postural changes in a tilt-table study including 10 subjects, and (ii) the effect of aging on the autonomic control using HR/RESP recordings from 40 healthy adults. Next, using HR/BP time series of over 450 adult ICU patients, we show that our technique can be used to reveal coupling changes associated with severe sepsis (AUC=0.74, sensitivity=0.74, specificity=0.60). Our findings indicate that reduced HR/MAP coupling is significantly associated with severe sepsis even after adjusting for clinical interventions (P<=0.001). These results demonstrate the utility of our approach in phenotyping complex vital-sign dynamics, and in providing mechanistic hypotheses in terms of break-down of autoregulatory systems under healthy and disease conditions.