New Measures of Heart Rate Variability Based on Subband Tachogram Complexity and Spectral Characteristics for Improved Stress and Anxiety Monitoring in Highly Ecological Settings
Bibliographic record
Abstract
Prediction of mental states, such as stress and anxiety, can be important in situations where reduced job performance due to increased mental strain can lead to critical situations (e.g., front-line healthcare workers and first responders). While recent advances in biomedical wearable sensor technologies have allowed for collection of multiple physiological signals in everyday environments, numerous challenges emerge from such uncontrolled settings, including increased noise levels and artifacts, confounding effects from other psychological states (e.g., mental fatigue), as well as physical variables (e.g., physical activity). These factors can be particularly detrimental for heart rate variability (HRV) measures which, in controlled settings, have been shown to accurately track stress and anxiety states. In this paper, we propose two new ways of computing HRV proxies which we show are more robust to such artifacts and confounding factors. The proposed features measure spectral and complexity properties of different aspects of the autonomic nervous system, as well as their interaction. Across two separate “in-the-wild” datasets, the proposed features showed to not only outperform benchmark HRV metrics, but to also provide complementary information, thus leading to significantly greater accuracy levels when fused together. Feature ranking analysis further showed the proposed features appearing in 45–64% of the top features, thus further emphasizing their importance. In particular, features derived from the high frequency band showed to be most important in the presence of fatigue and physical activity confounding factors, thus corroborating their importance for mental state assessment in highly ecological settings.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".