Sensitive Physiological Indices of Pain Based on Differential Characteristics of Electrodermal Activity

Overview

Journal IEEE Trans Biomed Eng

Specialties Biomedical Engineering
Biophysics

Date 2021 Mar 11

PMID 33705307

Citations 17

Authors

Youngsun Kong

Hugo F Posada-Quintero

Ki H Chon

Affiliations

Soon will be listed here.

Abstract

Objective: Electrodermal activity (EDA) has been widely used to assess human response to stressful stimuli, including pain. Recently, spectral analysis of EDA has been found to be more sensitive and reproducible for assessment of sympathetic arousal than traditional indices (e.g., tonic and phasic components). However, none of the aforementioned analyses incorporate the differential characteristics of EDA, which could be more sensitive to capturing fast-changing dynamics associated with pain responses.

Methods: We have tested the feasibility of using the derivative of phasic EDA and the modified time-varying spectral analysis of EDA. Sixteen subjects underwent four levels of pain stimulation using electric stimulation. Five-second segments of EDA were used for each level of stimulation, and pre-stimulation segments were considered stimulation level 0. We used support vector machines with the radial basis function kernel and multi-layer perceptron for three different scenarios of stimulation-level classification tasks: five stimulation levels (four levels of stimulation plus no stimulation); low, medium, and high pain stimulation (stimulation levels 0-1, 2, and 3-4, respectively); and high stimulation levels (stimulation levels 3-4) vs. no stimulation.

Results: The maximum balanced accuracies were 44% (five stimulation levels), 63% (for low, medium, and high pain stimulation), and 87% (sensitivity 83% and specificity 89%, for high stimulation vs. no stimulation).

Conclusion: The differential characteristics of EDA contributed highly to the accuracy of pain stimulation level detection of the classifiers. The external validity dataset was not considered in the study.

Significance: Our approach has the potential for accurate pain quantification using EDA.

Citing Articles

Detection of Cognitive Performance Deterioration Due to Cold-Air Exposure in Females Using Wearable Electrodermal Activity and Electrocardiogram.

Kong Y, McNaboe R, Hossain M, Posada-Quintero H, Diaz K, Chon K Biosensors (Basel). 2025; 15(2).

PMID: 39996980 PMC: 11852572. DOI: 10.3390/bios15020078.

Estimation of Pressure Pain in the Lower Limbs Using Electrodermal Activity, Tissue Oxygen Saturation, and Heart Rate Variability.

Kim Y, Pyo S, Lee S, Park C, Song S Sensors (Basel). 2025; 25(3).

PMID: 39943319 PMC: 11821113. DOI: 10.3390/s25030680.

Sex differences in autonomic functions and cognitive performance during cold-air exposure and cold-water partial immersion.

Kong Y, Hossain M, McNaboe R, Posada-Quintero H, Daley M, Diaz K Front Physiol. 2024; 15:1463784.

PMID: 39479308 PMC: 11521960. DOI: 10.3389/fphys.2024.1463784.

An Experimental and Clinical Physiological Signal Dataset for Automated Pain Recognition.

Gouverneur P, Badura A, Li F, Bienkowska M, Luebke L, Adamczyk W Sci Data. 2024; 11(1):1051.

PMID: 39333541 PMC: 11436824. DOI: 10.1038/s41597-024-03878-w.

Sensors and Devices Guided by Artificial Intelligence for Personalized Pain Medicine.

Xing Y, Yang K, Lu A, Mackie K, Guo F Cyborg Bionic Syst. 2024; 5:0160.

PMID: 39282019 PMC: 11395709. DOI: 10.34133/cbsystems.0160.

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