Three Distinct Patterns of Heart Rate and Heart Rate Variability Characterize Intra-Individual Variability in Resting Baseline Cardiac Activity

The common assumption that a resting baseline is a stable indicator of low physiological activity is challenged by the readily observed and often considerable variation in time series data. The variations are caused by dynamic shifts in the activity of the parasympathetic and sympathetic branches of the autonomic nervous system. A “resting” baseline may therefore be better conceptualized as a sequence of changing autonomic space modes. This possibility was examined in the present study using unsupervised machine learning methods to detect distinct patterns of cardiac indicators during resting baseline. The methods divided each resting baseline for 353 study participants into multiple segments, with each segment having piecewise stationarity for electrodermal activity. Subsequent cluster analyses of the segments revealed three distinct patterns of heart rate (HR) and heart rate variability (HRV) levels, labelled presence , stress , and absence . These patterns putatively reflect inner experiences that are exemplars of the three primary behavioral classes proposed within Polyvagal theory: social connection, mobilization, and immobilization. The HR and HRV levels observed in each pattern were consistent with prior psychophysiological observations linked to state mindfulness, stress, state dissociation, and vasovagal syncope. The findings provide suggestive evidence that intra-individual (i.e., within-person) variations in resting baseline data are physiological correlates of changing momentary psychological experience.