The role of Basal HRV assessed through wavelet transform in the prediction of anxiety and affect levels: a case study
Bibliographic record
Abstract
The present paper is a designed case study to understand the potential role of heart rate variability (HRV) to predict different levels of anxiety and affect in a non-clinical sample by Wavelet Transform Tools. Trait anxiety was evaluated through the Spielberger’s State-Trait Anxiety Inventory. Positive and negative affect scores were measured through the Positive (PA) and Negative (NA) Affect Schedule. Electrocardiogram (ECG) was recorded during 4 min in basal conditions. The ECG data was analyzed using Wavelet Transform Daubechies order 4 as kernel. Our aim is investigate whether HRV, assessed by the wavelet transform decomposition in 8 levels of frequency, would be able to characterize trait anxiety (TA), PA and NA characteristics. Correlation analysis were conducted between each psychological parameter (TA, PA and NA) and the values of frequency levels. The results showed a weak but relevant tendency between frequency level and individual trait or affective score. Thus, the present study suggests that resting HRV is efficient to predict anxiety trait and affective trait and state. Beyond, the results points to the need of introducing different stimulations or tasks capable of modulating HRV and evidencing its association with distinct psychophysiological patterns.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".