BAND: A Probabilistic Framework for Modeling Non-Stationary Heart Rate Variability in Rest–Stress–Rest Dynamics
Heart rate variability (HRV) forms the basis of non-invasive autonomic nervous system assessment. However, its analysis is constrained by the non-stationary nature of physiological signals. Standard analytical methods, which assume stationarity within fixed time windows, fail to capture dynamical effects of interest, such as the response to a physiological stressor. This limitation obstructs the development of mechanistic hypotheses about autonomic control. Here, we address this challenge by introducing a probabilistic framework for modeling non-stationary HRV dynamics during transient, single-event perturbation-recovery paradigms. We propose a hypothesis-driven, generative model that transforms the physiological response into a continuous-time stochastic process controlled by a double-logistic function. This approach deconstructs the R-R interval (RRi) series into a set of interpretable parameters representing the latency, rate, and magnitude of distinct response and recovery phases. Through simulation, we show that the model achieves high-fidelity parameter recovery and describes these dynamics more accurately than conventional fixed-time window methods under conditions matching its own generative assumptions. We then apply the framework to an empirical exercise-recovery recording, generating a precise, falsifiable hypothesis of “dissonant autonomic recovery”, where the baseline RR interval and its variability recover to distinct extents. The biphasic autonomic non-stationary decomposition (BAND) framework provides a formal methodology for translating RRi time series into quantitative, testable estimates of their generative processes.