Bayesian modeling of heartbeat evoked potentials (HEP) and heart rate variability (HRV) as biomarkers of spiritual and mental wellbeing; an exploratory study

Abstract

Spiritual and mental wellbeing are vital to health. Identification of their psychophysiological correlates enables their evidence-based integration into clinical therapeutics. We applied Bayesian modeling to assess HEPs and HRV as potential biomarkers of spiritual and mental wellbeing as it provides a flexible probabilistic approach. In this cross-sectional study, n = 30 adults completed spiritual (SIWB) and mental (WEMWBS) wellbeing questionnaires, then underwent electrophysiological recordings (EO, EC) with HRV and power spectral density analysis. Influence of difference of HEP and HRV on spiritual and mental wellbeing was analyzed. Bayesian regression identified the combined ΔHEP1 + ΔHEP3 model as the best predictor of spiritual well-being (BF10 = 8.75; R2 = 0.36), where strongest inclusion probability was observed for ΔHEP at Fz (Pincl =0.723), followed by ΔHEP at F7 (Pincl =0.617), with uncertain CrIs (95% CrI [−2.01, 0.05] and CrI [−0.62, 0.01] respectively) providing moderate-to-strong evidence for their inclusion. ΔHEP at F8 showed the strongest inclusion probability (P(incl|data) = 0.64; BF(inclusion) = 1.80), with posterior mean of 0.52 and a 95% CrI spanning [0.00, 1.61]. Demographic factors did not influence SIWB or WEMWBS scores. Bayesian model indicates that HEP can serve as a valuable tool to study psychophysiological modulation in spiritual and psychological interventions.