Anxiety has become a significant health concern affecting mental and physical well-being, with state anxiety, a transient emotional response, linked to adverse cardiovascular and long-term health outcomes. This research explores the potential of non-invasive wearable technology to enhance the real-time monitoring of physiological responses associated with state anxiety. Using electrooculography (EOG) and electrodermal activity (EDA), we have reviewed novel biomarkers that reveal nuanced emotional and stress responses. Our study presents two datasets: 1) EOG signal blink identification dataset BLINKEO, containing both true blink events and motion artifacts, and 2) EOG and EDA signals 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 state anxiety. 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 healthcare.

翻译：焦虑已成为影响身心健康的重要健康问题，其中状态焦虑作为一种短暂的情绪反应，与不良心血管及长期健康结果相关。本研究探索了无创可穿戴技术在增强状态焦虑相关生理反应实时监测方面的潜力。通过利用眼电图（EOG）和皮电活动（EDA），我们综述了能够揭示细微情绪与压力反应的新型生物标志物。本研究提出了两个数据集：1）包含真实眨眼事件与运动伪影的EOG信号眨眼识别数据集BLINKEO；2）记录冷加压试验（CPT）生理反应的EOG与EDA信号数据集EMOCOLD。通过分析眨眼率变异性、皮肤电导峰值及相关唤醒指标，我们识别出多种新的焦虑特异性生物标志物。采用SHapley可加性解释（SHAP）方法对模型进行解释与优化，从而可靠地理解与状态焦虑密切相关的生物标志物。这些结果表明，结合分析EOG与EDA数据可显著提升实时焦虑标志物的检测效果，突显了可穿戴设备在个性化健康监测与心理健康干预策略中的应用潜力。本工作推动了情境感知焦虑评估模型的发展，促进了可穿戴技术在医疗健康领域更有效的应用。