Using Electrooculography and Electrodermal Activity During a Cold Pressor Test to Identify Physiological Biomarkers of State Anxiety: Feature-Based Algorithm Development and Validation Study
Bibliographic record
Abstract
Background: Anxiety has become a significant health concern affecting mental and physical well-being, with state anxiety (s-anxiety)-a transient emotional response-linked to adverse cardiovascular and long-term health outcomes. Traditional physiological monitoring lacks the contextual sensitivity needed to assess anxiety in real time. Electrooculography (EOG) and electrodermal activity (EDA), 2 biosignals measurable by wearables, offer promising avenues for identifying biomarkers of s-anxiety in naturalistic environments. Objective: This study aims to identify novel biomarkers of s-anxiety using EOG and EDA signals collected in real-world conditions. We further explore how noninvasive wearable technology can enable real-time monitoring of physiological responses during induced stress, focusing on distinguishing true anxiety-related signals from artifacts in noisy environments. Methods: Our study presents two datasets: (1) the EOG signal blink identification dataset Blink Identification Electrooculography Dataset (BLINKEO), containing both true blink events and motion artifacts, and (2) the EOG and EDA signals dataset Emotion, Electrooculography, and Electrodermal Activity Monitoring in Cold Pressor Conditions Dataset (EMOCOLD), capturing physiological responses from a cold pressor test (CPT). From analyzing blink rate variability, skin conductance peaks, and associated arousal metrics, we identified multiple new anxiety-specific biomarkers. Shapley additive explanations (SHAP) were used to interpret and refine our model, enabling a robust understanding of the biomarkers that correlate strongly with s-anxiety. Results: BLINKEO feature analysis achieved a classification accuracy of 98.17% and F1-score of 0.87 in distinguishing blinks from noise. In the EMOCOLD, survey results confirmed elevated anxiety and affectivity during the CPT, which normalized during recovery. SHAP analysis revealed that specific EDA features (eg, Hjorth activity and spectral entropy) and EOG features (eg, opening phase energy and signal height) consistently contributed to accurate predictions of s-anxiety and affectivity. Contextual combinations of features outperformed single-feature analyses, revealing relationships critical for robust biomarker identification. Conclusions: These results suggest that a combined analysis of EOG and EDA data offers significant improvements in detecting real-time anxiety markers, underscoring the potential of wearables in personalized health monitoring and mental health intervention strategies. This work contributes to the development of context-sensitive models for anxiety assessment, promoting more effective applications of wearable technology in health care.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".