Point process temporal structure characterizes electrodermal activity
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26422.full.pdf
Description
Published version
Size
884.64 KB
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Adobe PDF
Checksum (MD5)
1cf9d0e6777ac3f0f46d4bdea278b236
Author(s)
Subramanian, Sandya
Barbieri, Riccardo
Brown, Emery Neal
Date Issued
October 2020
Journal
Proceedings of the National Academy of Sciences
Publisher
National Academy of Sciences
Citation
Subramanian, Sandya et al. "Point process temporal structure characterizes electrodermal activity." Proceedings of the National Academy of Sciences 117, 42 (October 2020): 26422-26428. © 2020 National Academy of Sciences
Version
Final published version
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Picower Institute for Learning and Memory
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DOI of Published Version
https://doi.org/10.1073/pnas.2004403117
