Behradfar Mohsen, Roy Shotabdi, Nuamah Joseph
School of Industrial Engineering and Management, Oklahoma State University, Stillwater, OK 74078, USA.
Sensors (Basel). 2025 Jul 3;25(13):4154. doi: 10.3390/s25134154.
The increasing prevalence of stress-related disorders necessitates accurate and efficient detection methods for timely intervention. This study explored the potential of heart rate variability as a biomarker for detecting mental stress using a publicly available dataset. A total of 93 heart rate variability features extracted from electrocardiogram signals were analyzed to differentiate stress from non-stress conditions. Our methodology involved data preprocessing, feature computation, and three feature selection strategies-filter-based, wrapper, and embedded-to identify the most relevant heart rate variability features. By leveraging Recursive Feature Elimination combined with Nested Leave-One-Subject-Out Cross-Validation, we achieved a peak F1 score of 0.76. The results demonstrate that two heart rate variability features-the median absolute deviation of the RR intervals (the time elapsed between consecutive R-waves on an electrocardiogram), which is normalized by the median, and the normalized low frequency power-consistently distinguished the stress states across multiple classifiers. To assess the robustness and generalizability of our best-performing model, we evaluated it on a completely unseen dataset, which resulted in an average F1 score of 0.63. These findings emphasize the value of targeted feature selection in optimizing stress detection models, particularly when handling high-dimensional datasets with potentially redundant features. This study contributes to the development of efficient stress monitoring systems, paving the way for improved mental health assessment and intervention.
与压力相关的疾病日益普遍，因此需要准确有效的检测方法以便及时进行干预。本研究利用一个公开可用的数据集，探讨了心率变异性作为检测精神压力生物标志物的潜力。对从心电图信号中提取的总共93个心率变异性特征进行了分析，以区分压力状态和非压力状态。我们的方法包括数据预处理、特征计算，以及三种特征选择策略——基于滤波器的、包装器的和嵌入式的，以识别最相关的心率变异性特征。通过利用递归特征消除结合嵌套留一被试交叉验证，我们获得了0.76的峰值F1分数。结果表明，两个心率变异性特征——RR间期（心电图上连续R波之间的时间间隔）的中位数绝对偏差（通过中位数进行归一化）和归一化低频功率，在多个分类器中始终能够区分压力状态。为了评估我们表现最佳的模型的稳健性和泛化能力，我们在一个完全未见过的数据集上对其进行了评估，得到的平均F1分数为0.63。这些发现强调了在优化压力检测模型时进行有针对性特征选择的价值，特别是在处理具有潜在冗余特征的高维数据集时。本研究为高效压力监测系统的开发做出了贡献，为改善心理健康评估和干预铺平了道路。
