Objective: The goal of this work was to develop a physiology-based paradigm for pulse selection from electrodermal activity (EDA) data. Methods: We aimed to use insight about the integrate-and-fire physiology of sweat gland bursts, which predicts inverse Gaussian inter-pulse interval structure. At the core of our paradigm is a subject-specific amplitude threshold selection process for pulses based on the statistical properties of four right-skewed models including the inverse Gaussian. These four models differ in their tail behavior, which reflects sweat gland physiology to varying degrees. By screening across thresholds and fitting all four models, we selected for heavier tails that reflect inverse Gaussian-like structure and verified the pulse selection with a goodness-of-fit analysis. Results: We tested our paradigm on two different subject cohorts recorded during different experimental conditions and using different equipment. In both cohorts, our method robustly and consistently recovered pulses that captured the inverse Gaussian-like structure predicted by physiology, despite large differences in noise level of the data. In contrast, an established EDA analysis paradigm, which assumes a constant amplitude threshold across all data, was unable to separate pulses from noise. Conclusion: We present a computationally efficient, statistically rigorous, and physiology-informed paradigm for pulse selection from EDA data that is robust across individuals and experimental conditions yet adaptable to changes in noise level. Significance: The robustness of our paradigm and its basis in physiology move EDA closer to serving as a clinical marker for sympathetic activity in diverse conditions such as pain, anxiety, depression, and sleep.

中文翻译：

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基于模型的皮肤电活动选择方法

目的：这项工作的目的是开发一种基于生理学的范例，用于从皮肤电活动（EDA）数据中选择脉冲。方法：我们的目的是利用有关汗腺爆发的整合和发射生理的见解，预测逆高斯脉冲间间隔结构。我们的范式的核心是基于包括逆高斯（Gaussian）在内的四个右偏模型的统计特性，针对脉冲的特定于对象的振幅阈值选择过程。这四个模型的尾巴行为不同，这在不同程度上反映了汗腺生理。通过跨阈值筛选并拟合所有四个模型，我们选择了反映反高斯型结构的较重尾巴，并通过拟合优度分析验证了脉冲选择。结果：我们在不同的实验条件和使用不同的设备记录的两个不同主题队列中测试了我们的范例。在这两个队列中，尽管数据的噪声水平差异很大，但我们的方法仍能可靠地，持续地恢复捕获生理学预测的高斯型逆结构的脉冲。相反，已建立的EDA分析范例无法在所有数据中分离出脉冲，该范例假设所有数据的幅度阈值都恒定。结论：我们为从EDA数据中选择脉冲提供了一种计算效率高，统计学上严格且具有生理学信息的范例，该范例在个体和实验条件下均很健壮，但可适应噪声水平的变化。意义：