Background and Objective

In recent years, stress and mental health have been considered as important worldwide concerns. Stress detection using physiological signals such as electrocardiogram (ECG), skin conductance (SC), electromyogram (EMG) and electroencephalogram (EEG) is a traditional approach. However, the effect of stress on the EMG signal of different muscles and the efficacy of combination of the EMG and other biological signals for stress detection have not been taken into account yet. This paper presents a comprehensive review of the EMG signal of the right and left trapezius and right and left erector spinae muscles for multi-level stress recognition. Also, the ECG signal was employed to evaluate the efficacy of EMG signals for stress detection.

Methods

Both EMG and ECG signals were acquired simultaneously from 34 healthy students (23 females and 11 males, aged 20-37 years). Mental arithmetic, Stroop color-word test, time pressure, and stressful environment were employed to induce stress in the laboratory.

Results

The accuracies of stress recognition in two, three and four levels were 100%, 97.6%, and 96.2%, respectively, obtained from the distinct combination of feature selection and machine learning algorithms.

Conclusions

The comparison of stress detection accuracies resulted from EMG and ECG indicators demonstrated the strong ability and the effectiveness of EMG signal for multi-level stress detection.

中文翻译：

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使用ECG和EMG信号进行压力检测：全面研究。

背景与目的

近年来，压力和心理健康已被视为世界范围内的重要问题。使用生理信号（例如心电图（ECG），皮肤电导（SC），肌电图（EMG）和脑电图（EEG））进行压力检测是一种传统方法。然而，尚未考虑到压力对不同肌肉的EMG信号的影响以及EMG和其他生物信号的组合对压力检测的功效。本文对左右斜方肌以及右竖立肌和左竖立肌的肌电信号进行了全面的综述，以进行多级应力识别。同样，ECG信号被用来评估EMG信号用于压力检测的功效。

方法

EMG和ECG信号是同时从34名健康学生（23位女性和11位男性，年龄20-37岁）中获取的。在实验室中采用心理算术，Stroop颜色词测试，时间压力和压力环境来诱发压力。

结果

从特征选择和机器学习算法的独特组合中获得的压力识别的两个，三个和四个级别的准确度分别为100％，97.6％和96.2％。

结论

由EMG和ECG指标得出的压力检测精度的比较表明，EMG信号对于多级压力检测具有强大的能力和有效性。