Yaghoubi Mahdis, Adib Navid, Monfared Abolfazl Rezaei, Tondashti Shirin Ashtari, Akhavan Saeed
Department of Engineering, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran.
Department of Engineering, School of Electrical Engineering, Sharif University of Technology, Tehran, Iran.
J Med Signals Sens. 2025 Aug 6;15:24. doi: 10.4103/jmss.jmss_71_24. eCollection 2025.
Stress, a widespread mental health concern, significantly impacts people well-being and performance. This study proposes a novel approach to stress detection by fusing cardiovascular and respiratory data.
Fifteen participants underwent a mental stress induction task while their electrocardiogram (ECG) and respiration signals were recorded. A real-time peak detection algorithm was developed for ECG signal processing, and both time and frequency domain features were extracted from ECG and respiration signals. Various machine learning models, including Support Vector Machine, K-Nearest Neighbors, bagged decision trees, and random forests, were employed for classification, with accurate labeling achieved through the NASA-TLX questionnaire.
The results demonstrate that combining respiration and cardiovascular features significantly enhances stress classification performance compared to using each modality alone, achieving an accuracy of 95.6% ±1.7%. Forward feature selection identifies key discriminative features from both modalities.
This study demonstrates the efficacy of multimodal physiological data integration for accurate stress detection, outperforming single-modality approaches and comparable studies in the literature. The findings highlight the potential of real-time monitoring systems in enhancing stress and health management.
压力是一个广泛存在的心理健康问题，会对人们的幸福感和表现产生重大影响。本研究提出了一种通过融合心血管和呼吸数据进行压力检测的新方法。
15名参与者在进行心理压力诱导任务时，记录其心电图（ECG）和呼吸信号。开发了一种用于ECG信号处理的实时峰值检测算法，并从ECG和呼吸信号中提取时域和频域特征。采用了包括支持向量机、K近邻、袋装决策树和随机森林在内的各种机器学习模型进行分类，并通过NASA-TLX问卷实现准确标注。
结果表明，与单独使用每种模态相比，结合呼吸和心血管特征可显著提高压力分类性能，准确率达到95.6%±1.7%。前向特征选择从两种模态中识别出关键判别特征。
本研究证明了多模态生理数据整合在准确压力检测方面的有效性，优于单模态方法以及文献中的可比研究。研究结果凸显了实时监测系统在加强压力和健康管理方面的潜力。
