Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, 60616, USA.
Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL, 60616, USA.
Comput Methods Programs Biomed. 2021 Feb;199:105898. doi: 10.1016/j.cmpb.2020.105898. Epub 2020 Dec 17.
In this work, we address the problem of detecting and discriminating acute psychological stress (APS) in the presence of concurrent physical activity (PA) using wristband biosignals. We focused on signals available from wearable devices that can be worn in daily life because the ultimate objective of this work is to provide APS and PA information in real-time management of chronic conditions such as diabetes by automated personalized insulin delivery. Monitoring APS noninvasively throughout free-living conditions remains challenging because the responses to APS and PA of many physiological variables measured by wearable devices are similar.
Various classification algorithms are compared to simultaneously detect and discriminate the PA (sedentary state, treadmill running, and stationary bike) and the type of APS (non-stress state, mental stress, and emotional anxiety). The impact of APS inducements is verified with commonly used self-reported questionnaires (The State-Trait Anxiety Inventory (STAI)). To aid the classification algorithms, novel features are generated from the physiological variables reported by a wristband device during 117 hours of experiments involving simultaneous APS inducement and PA. We also translate the APS assessment into a quantitative metric for use in predicting the adverse outcomes.
An accurate classification of the concurrent PA and APS states is achieved with an overall classification accuracy of 99% for PA and 92% for APS. The average accuracy of APS detection during sedentary state, treadmill running, and stationary bike is 97.3, 94.1, and 84.5%, respectively.
The simultaneous assessment of APS and PA throughout free-living conditions from a convenient wristband device is useful for monitoring the factors contributing to an elevated risk of acute events in people with chronic diseases like cardiovascular complications and diabetes.
在这项工作中，我们致力于利用腕带生物信号解决在同时存在身体活动（PA）的情况下检测和区分急性心理应激（APS）的问题。我们专注于可穿戴设备中可用的信号，这些信号可在日常生活中佩戴，因为这项工作的最终目标是通过自动化个性化胰岛素输送，为慢性疾病（如糖尿病）的实时管理提供 APS 和 PA 信息。在自由生活条件下进行非侵入性的 APS 监测仍然具有挑战性，因为可穿戴设备测量的许多生理变量对 APS 和 PA 的反应相似。
比较了各种分类算法，以同时检测和区分 PA（静坐状态、跑步机跑步和固定自行车）和 APS 类型（非应激状态、心理应激和情绪焦虑）。使用常用的自我报告问卷（状态-特质焦虑量表（STAI））验证 APS 诱发的影响。为了帮助分类算法，从腕带设备在涉及同时 APS 诱发和 PA 的 117 小时实验中报告的生理变量生成新的特征。我们还将 APS 评估转换为一种定量指标，用于预测不良结果。
通过对 PA 和 APS 状态进行整体分类精度为 99%的 PA 和 92%的 APS，实现了对同时 PA 和 APS 状态的准确分类。在静坐、跑步机跑步和固定自行车状态下，APS 检测的平均准确率分别为 97.3%、94.1%和 84.5%。
从方便的腕带设备在自由生活条件下同时评估 APS 和 PA，对于监测导致心血管并发症和糖尿病等慢性疾病患者急性事件风险增加的因素是有用的。
