A Model-Based Framework for Assessing the Physiologic Structure of Electrodermal Activity
Name
A_Model-Based_Framework_for_Assessing_the_Physiologic_Structure_of_Electrodermal_Activity.pdf
Description
Published version
Size
7.05 MB
Format
Adobe PDF
Checksum (MD5)
64fd8be0db6dbf6a662e16173e10f7b5
Author(s)
Subramanian, Sandya
Purdon, Patrick L
Barbieri, Riccardo
Brown, Emery Neal
Date Issued
2021
Journal
IEEE Transactions on Biomedical Engineering
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Subramanian, Sandya, Purdon, Patrick L, Barbieri, Riccardo and Brown, Emery N. 2021. "A Model-Based Framework for Assessing the Physiologic Structure of Electrodermal Activity." IEEE Transactions on Biomedical Engineering, 68 (9).
Version
Final published version
Abstract
OBJECTIVE: We present a statistical model for extracting physiologic characteristics from electrodermal activity (EDA) data in observational settings. METHODS: We based our model on the integrate-and-fire physiology of sweat gland bursts, which predicts inverse Gaussian (IG) inter-pulse interval structure. At the core of our model-based paradigm is a subject-specific amplitude threshold selection process for EDA pulses based on the statistical properties of four right-skewed models including the IG. By performing a sensitivity analysis across thresholds and fitting all four models, we selected for IG-like structure and verified the pulse selection with a goodness-of-fit analysis, maximizing capture of physiology at the time scale of EDA responses. RESULTS: We tested the model-based paradigm on simulated EDA time series and data from two different experimental cohorts recorded during different experimental conditions, using different equipment. In both the simulated and experimental data, our model-based method robustly recovered pulses that captured the IG-like structure predicted by physiology, despite large differences in noise level. In contrast, established EDA analysis tools, which attempted to estimate neural activity from slower EDA responses, did not provide physiological validation and were susceptible to 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 varying noise level. SIGNIFICANCE: The robustness of the model-based paradigm and its physiological basis provide empirical support for the use of EDA as a clinical marker for sympathetic activity in conditions such as pain, anxiety, depression, and sleep states.
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
Picower Institute for Learning and Memory
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/TBME.2021.3071366
