A Model-Based Framework for Assessing the Physiologic Structure of Electrodermal Activity

Abstract

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 subjectspecific 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 physiologyinformed 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.

Quantitative assessment of the relationship between behavioral and autonomic dynamics during propofol-induced unconsciousness

“…This structure suggests that the inter-pulse intervals in EDA data, much like RR intervals in ECG, can be hypothesized to follow an inverse Gaussian distribution. Based on this observation, we have also designed a physiology-informed framework for extracting pulses from phasic EDA data that has been validated in awake subjects and those under propofol sedation [ 24 ]. In this work, we adapted the same history-dependent inverse Gaussian framework from HRV to the inter-pulse intervals in EDA to compute instantaneous estimates of mean and standard deviation of pulse rate in EDA [ 11 , 12 ].…”

Elementary integrate-and-fire process underlies pulse amplitudes in Electrodermal activity

“…Pulse selection was done using the methodology described in [25] in which the fits of four right-skewed models were used to select the best prominence threshold at which to extract pulses. Prominence is a locally adjusted amplitude measure computed using the findpeaks algorithm in Matlab.…”

“…The valleys are chosen based on the lowest point in the signal between the peak and the next intersection with the signal of equal height on either side. Since the same pulse selection framework was followed on the same two cohorts of data, the pulses selected for each subject were also the same as in [25]. The final thresholds used for each subject and temporal properties of the pulses selected can be found in [25].…”

“…Since the same pulse selection framework was followed on the same two cohorts of data, the pulses selected for each subject were also the same as in [25]. The final thresholds used for each subject and temporal properties of the pulses selected can be found in [25]. For this paper, we used the prominence of the extracted pulses as the measure of pulse amplitude.…”

“…We showed that deviations from the inverse Gaussian due to recording across many sweat glands tend toward right-skewed heavier tailed distributions, such as the lognormal [23]. Using these insights, we further defined a low-order paradigm for verifying the physiologic structure in EDA that includes a framework for goodness-of-fit analysis [25,26].…”