Objective Pain Measurement based on Physiological Signals. Issue 1 (June 2018)

• Record Type: Journal Article
• Authors: Lin, Yingzi; Wang, Li; Xiao, Yan; Urman, Richard D.; Dutton, Richard; Ramsay, Michael
• Is Part Of: Proceedings of the International Symposium of Human Factors and Ergonomics in Healthcare. Volume 7:Issue 1(2018)
• Page Start: 240
• Page End: 247
• Publication Date: 2018-06
• Subjects: Objective Pain Measurement -- Physiological Signals -- Electroencephalography -- Skin Conductance -- Facial Expression; Human engineering -- Periodicals; Medical care -- Periodicals; 620.8
• DOI: 10.1177/2327857918071056
• Languages: English
• ISSNs: 2327-8579

Abstract

In an attempt to understand human physiological signals when an individual is subjected to pain, we set up a tonic pain experiment in a laboratory setting. The subjects' physiological signals were recorded, timestamped, and compared to an initial 30 second baseline measurement. Subjects were also asked to verbally state their level of pain based on a visual analog scale in order to compare reported pain levels with physiological signals. The physiological signals measured were: Electroencephalography (EEG), Pupillary Unrest Under Ambient Light (PUAL), Skin Conductance (SC), Electromyography (EMG), Respiration Rate (RR), Blood Volume Pulse (BVP), Skin Temperature (ST), Blood Pressure (BP), and Facial Expression (FE). ANOVA and frequency domain analyses were conducted on the data in order to determine whether there was a significant difference between the 'pain' and 'no pain' (baseline) states of an individual. Based on our results, skin conductance, PUAL, facial expression, and EEG signals were theorized to be good signals for the classification of tonic pain, or any pain applied directly to an individual.