Electrodermal activity (EDA) is a direct readout of the body’s sympathetic nervous system measured as sweat-induced changes in the skin’s electrical conductance. There is growing interest in using EDA to track physiological conditions such as stress levels, sleep quality, and emotional states. Standardized EDA data analysis methods are readily available. However, none considers an established physiological feature of EDA. The sympathetically mediated pulsatile changes in skin sweat measured as EDA resemble an integrate-and-fire process. An integrate-and-fire process modeled as a Gaussian random walk with drift diffusion yields an inverse Gaussian model as the interpulse interval distribution. Therefore, we chose an inverse Gaussian model as our principal probability model to characterize EDA interpulse interval distributions. To analyze deviations from the inverse Gaussian model, we considered a broader model set: the generalized inverse Gaussian distribution, which includes the inverse Gaussian and other diffusion and nondiffusion models; the lognormal distribution which has heavier tails (lower settling rates) than the inverse Gaussian; and the gamma and exponential probability distributions which have lighter tails (higher settling rates) than the inverse Gaussian. To assess the validity of these probability models we recorded and analyzed EDA measurements in 11 healthy volunteers during 1 h of quiet wakefulness. Each of the 11 time series was accurately described by an inverse Gaussian model measured by Kolmogorov–Smirnov measures. Our broader model set offered a useful framework to enhance further statistical descriptions of EDA. Our findings establish that a physiologically based inverse Gaussian probability model provides a parsimonious and accurate description of EDA.

皮肤电活动（EDA）是人体交感神经系统的直接读数，以汗液引起的皮肤电导率变化来衡量。使用EDA跟踪诸如压力水平，睡眠质量和情绪状态等生理状况的兴趣日益浓厚。可以使用标准化的EDA数据分析方法。但是，没有人认为EDA具有确定的生理特征。EDA所测量的皮肤汗液中由交感神经介导的搏动性变化类似于“整合并发射”过程。建模为具有漂移扩散的高斯随机游走的积分并发射过程会产生一个反高斯模型作为脉冲间隔分布。因此，我们选择逆高斯模型作为我们的主要概率模型来表征EDA脉冲间隔分布。为了分析与反高斯模型的偏差，我们考虑了更广泛的模型集：广义反高斯分布，其中包括反高斯模型以及其他扩散和非扩散模型；对数正态分布的尾部比高斯逆数要重（沉降率较低）；伽玛和指数概率分布的尾部比逆高斯分布的尾部更轻（稳定度更高）。为了评估这些概率模型的有效性，我们在安静的清醒1小时内记录并分析了11名健康志愿者的EDA测量值。用Kolmogorov–Smirnov测度测量的逆高斯模型可以准确地描述这11个时间序列。我们更广泛的模型集提供了一个有用的框架，可以增强对EDA的进一步统计描述。