Sensitive Physiological Indices of Pain Based on Differential Characteristics of Electrodermal Activity.
Sensitive Physiological Indices of Pain Based on Differential Characteristics of Electrodermal Activity.
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DOI：
10.1109/tbme.2021.3065218
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发表时间：
2021-10
期刊：
影响因子：
--
通讯作者：
Chon KH
中科院分区：
文献类型：
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作者：
Kong Y;Posada-Quintero HF;Chon KH
皮肤电活动（EDA）已被广泛用于评估人类对压力刺激的反应，包括疼痛。最近，人们发现EDA的频谱分析在评估交感神经觉醒方面比传统的指标（例如，张力和相位成分）更加敏感和可重复性。然而，上述分析都没有纳入EDA的差异特征，EDA可能更敏感地捕捉与疼痛反应相关的快速变化动态。我们测试了利用相位EDA的导数和改进的EDA的时变谱分析的可行性。16名受试者使用电刺激进行了四个级别的疼痛刺激。每个刺激级别使用5秒的EDA片段，刺激前的片段被认为是刺激级别0。我们使用径向基函数核支持向量机和多层感知机对三种不同场景的刺激级别分类任务进行了研究：五种刺激级别（四种刺激加无刺激）；低、中、高疼痛刺激（刺激水平分别为0-1、2和3-4）；高刺激水平（刺激水平3-4）vs.无刺激。最大平衡准确度为44%（五个刺激水平），63%（低、中、高疼痛刺激）和87%（高刺激与无刺激的敏感性83%和特异性89%）。EDA的差异特征对分类器检测疼痛刺激水平的准确性有很大贡献。本研究未考虑外部效度数据集。我们的方法具有使用EDA准确量化疼痛的潜力。
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.