The prevalence of metabolic disorders has increased rapidly as such they become a major health issue recently. Despite the definition of genetic associations with obesity and cardiovascular diseases, they constitute only a small part of the incidence of disease. Environmental and physiological effects such as stress, behavioral and metabolic disturbances, infections, and nutritional deficiencies have now revealed as contributing factors to develop metabolic diseases. This study presents a multivariate methodology for the modeling of stress on metabolic syndrome (MES) patients. We have developed a supporting system to cope with MES patients’ anxiety and stress by means of several biosignals such as ECG, GSR, body temperature, SpO${}_2$, glucose level, and blood pressure that are measured by a wearable device. We employed a neural network model to classify emotions with HRV analysis in the detection of stressor moments. We have accurately recognized the stressful situations using physiological responses to stimuli by utilizing our proposed affective state detection algorithm. We evaluated our system with a dataset of 312 biosignal records from 30 participants and the results showed that our proposed method achieved an average accuracy of 92% and 89% in distinguishing stress level in MES and other groups respectively. Both being the focus of an MES group and others proved to be highly arousing experiences which were significantly reflected in the physiological signal. Exposure to the stress in MES and cardiovascular heart disease patients increases the chronic symptoms. An early stage of comprehensive intervention may reduce the risk of general cardiovascular events in these particular groups. In this context, the use of e-health applications such as our proposed system facilitates these processes.

代谢紊乱的患病率迅速增加，因此它们最近成为一个主要的健康问题。尽管定义了与肥胖和心血管疾病的遗传关联，但它们仅占疾病发病率的一小部分。环境和生理影响，如压力、行为和代谢紊乱、感染和营养缺乏，现已揭示为导致代谢疾病的因素。本研究提出了一种多变量方法，用于对代谢综合征 (MES) 患者的压力进行建模。我们开发了一套支持系统，通过ECG、GSR、体温、SpO等多种生物信号来应对MES患者的焦虑和压力${}_2$可穿戴设备测量的血糖水平和血压。我们采用神经网络模型通过 HRV 分析对情绪进行分类，以检测压力源时刻。通过利用我们提出的情感状态检测算法，我们使用对刺激的生理反应准确地识别了压力情况。我们使用来自 30 名参与者的 312 条生物信号记录的数据集评估了我们的系统，结果表明，我们提出的方法在区分 MES 和其他组的压力水平方面分别达到了 92% 和 89% 的平均准确率。作为 MES 组和其他组的焦点都被证明是高度唤醒的体验，这些体验在生理信号中得到了显着反映。暴露于 MES 和心血管心脏病患者的压力会增加慢性症状。综合干预的早期阶段可能会降低这些特定人群发生一般心血管事件的风险。在这种情况下，使用电子健康应用程序（例如我们提议的系统）可以促进这些过程。