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.
Matías Castillo-Aguilar, David Medina-Ortiz, Ruby Méndez Muñoz et al.· Technologies· 0 citations
Teaching is a high-demand occupation in which sustained stress may impair wellbeing and self-regulatory processes. Within the neurovisceral integration framework, heart rate variability (HRV) is used as an indicator of autonomic modulation relevant to self-control. To examine the relationships among perceived stress, social support, resting HRV, and executive function performance (Stroop and verbal fluency) in teachers, considering burnout and quality of life as concurrent variables.
Cross-sectional correlational study (
N
= 49). Participants completed the Perceived Stress Scale (PSS-10), Maslach Burnout Inventory-Educators Survey (MBI-ES), WHO Quality of Life-BREF (WHOQOL-BREF), and the Multidimensional Scale of Perceived Social Support (MSPSS). HRV was recorded for 5 min at seated rest using a Polar H10 chest strap and processed into time-domain indices (mean RR, SDNN, RMSSD) and frequency-domain indices (LF, HF, LF/HF). Executive performance was assessed with the Stroop test and phonemic and semantic verbal fluency. Associations were modeled using hierarchical multivariate Bayesian regression adjusted for potential confounders.
Multivariate models suggested that global variability markers (e.g., SDNN and mean RR) were associated with better Stroop interference performance, whereas indices reflecting parasympathetic predominance (e.g., HF and a parasympathetic nervous system index) were associated with poorer interference performance. Higher perceived stress and higher depersonalization were associated with poorer verbal fluency (semantic and phonemic) and poorer inhibitory control as measured by Stroop.
In this exploratory study, preliminary associations suggested that executive performance in teachers may relate to both psychological state (perceived stress and burnout) and resting autonomic modulation. Notably, higher resting parasympathetic indices showed a tendency toward negative associations with executive performance, a pattern that contradicts simple interpretations of neurovisceral integration and warrants replication. These findings should be regarded as hypothesis-generating signals pending confirmation in larger, longitudinal samples.
Leonardo Alberto Velásquez-Castro, Matías Castillo-Aguilar, José Adrián Paredes-Águila et al.· Frontiers in Education· 0 citations
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