Bayesian modeling of heartbeat evoked potentials (HEP) and heart rate variability (HRV) as biomarkers of spiritual and mental wellbeing; an exploratory study
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".