IEEE Trans Biomed Eng. 2021 Oct;68(10):3122-3130. doi: 10.1109/TBME.2021.3065218. Epub 2021 Sep 20.
Electrodermal activity (EDA) has been widely used to assess human response to stressful stimuli, including pain. Recently, spectral analysis of EDA has been found to be more sensitive and reproducible for assessment of sympathetic arousal than traditional indices (e.g., tonic and phasic components). However, none of the aforementioned analyses incorporate the differential characteristics of EDA, which could be more sensitive to capturing fast-changing dynamics associated with pain responses.
We have tested the feasibility of using the derivative of phasic EDA and the modified time-varying spectral analysis of EDA. Sixteen subjects underwent four levels of pain stimulation using electric stimulation. Five-second segments of EDA were used for each level of stimulation, and pre-stimulation segments were considered stimulation level 0. We used support vector machines with the radial basis function kernel and multi-layer perceptron for three different scenarios of stimulation-level classification tasks: five stimulation levels (four levels of stimulation plus no stimulation); low, medium, and high pain stimulation (stimulation levels 0-1, 2, and 3-4, respectively); and high stimulation levels (stimulation levels 3-4) vs. no stimulation.
The maximum balanced accuracies were 44% (five stimulation levels), 63% (for low, medium, and high pain stimulation), and 87% (sensitivity 83% and specificity 89%, for high stimulation vs. no stimulation).
The differential characteristics of EDA contributed highly to the accuracy of pain stimulation level detection of the classifiers. The external validity dataset was not considered in the study.
Our approach has the potential for accurate pain quantification using EDA.
皮肤电活动（EDA）已广泛用于评估人类对包括疼痛在内的应激刺激的反应。最近，EDA 的频谱分析被发现比传统指标（如紧张和相位成分）更能敏感和可重复地评估交感神经兴奋。然而，上述分析均未纳入 EDA 的差异特征，这些特征可能更能敏感地捕捉与疼痛反应相关的快速变化动态。
我们测试了使用相位 EDA 的导数和 EDA 的改进时变频谱分析的可行性。16 名受试者接受了四种不同强度的电刺激疼痛刺激。每个刺激水平使用 5 秒的 EDA 段，刺激前的 EDA 段被认为是刺激水平 0。我们使用带有径向基函数核和多层感知器的支持向量机进行了三种不同的刺激水平分类任务：五个刺激水平（四个刺激水平加无刺激）；低、中、高强度疼痛刺激（刺激水平 0-1、2 和 3-4）；以及高强度刺激（刺激水平 3-4）与无刺激。
最大平衡准确率分别为 44%（五个刺激水平）、63%（低、中、高强度疼痛刺激）和 87%（高刺激 vs. 无刺激，敏感性 83%，特异性 89%）。
EDA 的差异特征对分类器的疼痛刺激水平检测准确性有很大贡献。研究未考虑外部有效性数据集。
我们的方法有可能使用 EDA 进行准确的疼痛量化。
