Abstract
The automatic detection of facial expressions of pain is needed to ensure accurate pain assessment of patients who are unable to self-report pain. To overcome the challenges of automatic systems for determining pain levels based on facial expressions in clinical patient monitoring, a surface electromyography method was tested for feasibility in healthy volunteers. In the current study, two types of experimental gradually increasing pain stimuli were induced in thirty-one healthy volunteers who attended the study. We used a surface electromyography method to measure the activity of five facial muscles to detect facial expressions during pain induction. Statistical tests were used to analyze the continuous electromyography data, and a supervised machine learning was applied for pain intensity prediction model. Muscle activation of corrugator supercilii was most strongly associated with self-reported pain, and the levator labii superioris and orbicularis oculi showed a statistically significant increase in muscle activation when the pain stimulus reached subjects’ self -reported pain thresholds. The two strongest features associated with pain, the waveform length of the corrugator supercilii and levator labii superioris, were selected for a prediction model. The performance of the pain prediction model resulted in a c-index of 0.64. In the study results, the most detectable difference in muscle activity during the pain experience was connected to eyebrow lowering, nose wrinkling and upper lip raising. As the performance of the prediction model remains modest, yet with a statistically significant ordinal classification, we suggest testing with a larger sample size to further explore the variables that affect variation in expressiveness and subjective pain experience.
Highlights
Pain as a subjective experience is difficult to assess in situations in which patients have no ability to self-report their pain
These situations are common in intensive care units (ICUs), where pain related to critical illnesses, major surgeries and everyday care procedures is common [1,2]
In our results the facial muscles most associated with pain were congruent to the muscle actions described as the core facial pain expression in existing literature [11,12,17]
Summary
The main objective of this study was to evaluate the feasibility of the sEMG method for pain detection using two kinds of gradually increasing pain stimuli in healthy subjects. Demographic information collected from the subjects consisted of age and sex, as we aimed to have equal numbers of male and female subjects in our sample
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