论文标题
审查生物生理压力监测中使用的分类技术
Review on Classification Techniques used in Biophysiological Stress Monitoring
论文作者
论文摘要
心血管活动与在应力条件下身体的反应直接相关。压力基于其强度，可以分为两种类型，即急性应激（短期应激）和慢性应激（长期压力）。反复的急性应激和连续的慢性应激可能在循环系统的炎症中起着至关重要的作用，从而导致心脏病发作或中风。在这项研究中，我们审查了应用于压力监测设备中使用的不同压力指示参数的常用机器学习分类技术。这些参数包括光摄像仪（PPG），心电图（ECG），肌电图（EMG），电力皮肤反应（GSR），心率变化（HRV），皮肤温度，呼吸速率，电脑电图（EEG）和唾液皮质醇，用于压力监测设备。这项研究还提供了有关选择分类器的讨论，该分类器取决于精度以外的许多因素，例如实验中涉及的受试者的数量，信号处理类型和计算限制。
Cardiovascular activities are directly related to the response of a body in a stressed condition. Stress, based on its intensity, can be divided into two types i.e. Acute stress (short-term stress) and Chronic stress (long-term stress). Repeated acute stress and continuous chronic stress may play a vital role in inflammation in the circulatory system and thus leads to a heart attack or to a stroke. In this study, we have reviewed commonly used machine learning classification techniques applied to different stress-indicating parameters used in stress monitoring devices. These parameters include Photoplethysmograph (PPG), Electrocardiographs (ECG), Electromyograph (EMG), Galvanic Skin Response (GSR), Heart Rate Variation (HRV), skin temperature, respiratory rate, Electroencephalograph (EEG) and salivary cortisol, used in stress monitoring devices. This study also provides a discussion on choosing a classifier, which depends upon a number of factors other than accuracy, like the number of subjects involved in an experiment, type of signals processing and computational limitations.
