A Model-Based Approach for Pulse Selection from Electrodermal Activity

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 process 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 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 reflect sweat gland physiology to varying degrees. By screening across thresholds and fitting all four models, we selected for heavier tails that reflect inverse Gaussianlike structure and verified the pulse selection with a goodness-of-fit analysis. Results: We tested our paradigm on two different cohorts of data recorded during different 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 was unable to separate pulses from noise due to assuming a constant amplitude threshold across all data. Conclusion: We present a computationally efficient, statistically rigorous, and physiology-informed paradigm for pulse selection from EDA data that is robust across individuals and 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.