使用智能手表生理和活动数据检测创伤后应激障碍过度唤醒事件
Posttraumatic Stress Disorder Hyperarousal Event Detection Using Smartwatch Physiological and Activity Data
摘要：
创伤后应激障碍（PTSD）是一种精神疾病，影响了近四分之一从战区返回的美国退伍军人。创伤后应激障碍的治疗通常包括疗程治疗和药物治疗的结合。然而，患者经常在治疗过程之外经历最严重的PTSD症状。移动保健应用程序可能会解决这一差距，但其有效性受到目前在持续监测和检测能力方面的差距的限制，从而无法及时采取干预措施。本文的目标是开发一种新的方法来检测过度觉醒事件使用生理和基于活动的机器学习算法。包括心率和身体加速度以及自我报告的过度觉醒事件在内的生理数据是使用为商业现成的可穿戴设备开发的工具从99名美国退伍军人中收集的，这些退伍军人在几天内被诊断患有创伤后应激障碍。这些数据被用来开发四种机器学习算法：随机森林，支持向量机，逻辑回归和XGBoost。XGBoost模型在检测PTSD症状发作方面表现最好，准确率超过83%，AUC为0.70。事后SHapley加性解释（SHAP）加性解释分析表明，算法预测与平均心率、最小心率和平均身体加速度相关。研究结果显示，在检测PTSD症状的发病，这可能是开发远程和持续监测系统的PTSD的基础上的承诺。这样的系统可以解决一个重要的差距，及时干预PTSD自我管理以外的预定的临床预约。
英文摘要：
Posttraumatic Stress Disorder (PTSD) is a psychiatric condition affecting nearly a quarter of the United States war veterans who return from war zones. Treatment for PTSD typically consists of a combination of in-session therapy and medication. However; patients often experience their most severe PTSD symptoms outside of therapy sessions. Mobile health applications may address this gap, but their effectiveness is limited by the current gap in continuous monitoring and detection capabilities enabling timely intervention. The goal of this article is to develop a novel method to detect hyperarousal events using physiological and activity-based machine learning algorithms. Physiological data including heart rate and body acceleration as well as self-reported hyperarousal events were collected using a tool developed for commercial off-the-shelf wearable devices from 99 United States veterans diagnosed with PTSD over several days. The data were used to develop four machine learning algorithms: Random Forest, Support Vector Machine, Logistic Regression and XGBoost. The XGBoost model had the best performance in detecting onset of PTSD symptoms with over 83% accuracy and an AUC of 0.70. Post-hoc SHapley Additive exPlanations (SHAP) additive explanation analysis showed that algorithm predictions were correlated with average heart rate, minimum heart rate and average body acceleration. Findings show promise in detecting onset of PTSD symptoms which could be the basis for developing remote and continuous monitoring systems for PTSD. Such systems may address a vital gap in just-in-time interventions for PTSD self-management outside of scheduled clinical appointments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
