Acute pain intensity monitoring with the classification of multiple physiological parameters

Abstract

Current acute pain intensity assessment tools are mainly based on self-reporting by patients, which is impractical for non-communicative, sedated or critically ill patients. In previous studies, various physiological signals have been observed qualitatively as a potential pain intensity index. On the basis of that, this study aims at developing a continuous pain monitoring method with the classification of multiple physiological parameters. Heart rate (HR), breath rate (BR), galvanic skin response (GSR) and facial surface electromyogram were collected from 30 healthy volunteers under thermal and electrical pain stimuli. The collected samples were labelled as no pain, mild pain or moderate/severe pain based on a self-reported visual analogue scale. The patterns of these three classes were first observed from the distribution of the 13 processed physiological parameters. Then, artificial neural network classifiers were trained, validated and tested with the physiological parameters. The average classification accuracy was 70.6%. The same method was applied to the medians of each class in each test and accuracy was improved to 83.3%. With facial electromyogram, the adaptivity of this method to a new subject was improved as the recognition accuracy of moderate/severe pain in leave-one-subject-out cross-validation was promoted from 74.9 ± 21.0 to 76.3 ± 18.1%. Among healthy volunteers, GSR, HR and BR were better correlated to pain intensity variations than facial muscle activities. The classification of multiple accessible physiological parameters can potentially provide a way to differentiate among no, mild and moderate/severe acute experimental pain.

Developing a pain intensity prediction model using facial expression: A feasibility study with electromyography

“…The facial sEMG was recorded among other biosignals including heart rate, respiratory rate and skin conductance. The procedures are described and reported with greater detail in Jiang et al [31]. The study procedures were conducted in a quiet room with a comfortable armchair.…”

“…The study subjects may have applied different emotion regulation strategies during pain inducement that can moderate their pain expression [57]. However, in addition to facial electrodes, the participants wore electrodes on their fingers and in a belt around their chests to detect other biosignals (Reported by Jiang et al [31]). This might have distracted them from concentrating on the facial expressions only.…”

Pain Detection with Bioimpedance Methodology from 3-Dimensional Exploration of Nociception in a Postoperative Observational Trial

“…Although several commercially devices [2,8,9], as well as newly researched principles [10,11], are currently available and clinically evaluated in many trials, no monitor is widely and routinely used for analgesia assessment [12]. Hence, what differentiates Anspec-pro from the other available devices is the mathematical formalism for parametric modelling of time-frequency variations of skin impedance values [13].…”

EEG beta-band spectral entropy predicts the effects of drug treatment on patients with herpes zoster

“…The effectiveness of medication on pain is generally evaluated as the degree of decrease in patients' subjective pain ratings after treatment. Although self-report is still the golden-standard for pain assessment in clinical practice, a variety of physiological correlates of pain have been found in past decades [15][16][17] . Particularly, a huge body of studies based on electroencephalography (EEG) have shown that pain perception is closely associated with certain types of brain rhythmical activities [18][19][20] .…”