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

• Published in: IEEE transactions on biomedical engineering Vol. 68; no. 10; pp. 3122 - 3130
• Main Authors: Kong, Youngsun, Posada-Quintero, Hugo F., Chon, Ki H.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.10.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Arousal; Biomedical measurement; Electrodermal activity; Electrodes; Heuristic algorithms; Human response; machine learning; Multilayers; Pain; Pain sensitivity; phasic component; Radial basis function; Resonant frequency; Segments; Spectral analysis; Spectrum analysis; Stimulation; Support vector machines; Time-frequency analysis; tonic component
• DOI: 10.1109/TBME.2021.3065218
• ISSN: 0018-9294; 1558-2531

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.