EDA as a Discriminate Feature in Computation of Mental Stress
EDA as a Discriminate Feature in Computation of Mental Stress
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EDA 作为精神压力计算中的判别特征
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
K. Masood
中科院分区：
文献类型：
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作者：
K. Masood
在计算心理压力时，各种特征是从一系列生理信号中确定的。在应激期间，应激者体内的荷尔蒙水平会发生变化，从而导致许多生物医学信号在不同的身体器官之间传递。一种无线可穿戴平台已经设计出来，可以记录这些生物医学信号。为了诱导压力，研究人员开展了一系列认知实验，这些实验会给参与者带来压力。EDA、HRV、呼吸和大脑信号用于计算特征，目的是识别最显著的特征或它们的各种组合。经验证，EDA特征达到了使用各种特征组合或使用包含所有特征的主集可以获得的类似精度。采用含有径向基核的支持向量机模型，EDA的分类正确率在80%以上。电子健康；心理应激；生物医学信号；EDA；支持向量机；无线传感器
In computation of mental stress, various features are determined from a range of physiological signals. During stress, hormones levels inside the body of a stressed person are changed that results in a number of biomedical signals that are communicated among different body organs. A wireless wearable platform has been designed that record these biomedical signals. To induce stress, a series of cognitive experiments were developed that produce stress on the participants. EDA, HRV, respiration and brain signals are used for computing features and the objective was to identify most significant feature or their various combinations. It is verified that EDA features achieves a similar accuracy that can be obtained using various combination of features or using a master set containing all the features. The classification accuracy is more than 80% using EDA with a SVM model containing rbf kernel. Keywords-E-health; mental stress; bionedical signals; EDA; SVM; wireless sensors