Fernandez Rojas Raul, Hirachan Niraj, Brown Nicholas, Waddington Gordon, Murtagh Luke, Seymour Ben, Goecke Roland
Human-Centred Technology Research Centre, Faculty of Science and Technology, University of Canberra, Canberra, ACT, Australia.
Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia.
Front Pain Res (Lausanne). 2023 Jun 19;4:1150264. doi: 10.3389/fpain.2023.1150264. eCollection 2023.
Pain assessment is a challenging task encountered by clinicians. In clinical settings, patients' self-report is considered the gold standard in pain assessment. However, patients who are unable to self-report pain are at a higher risk of undiagnosed pain. In the present study, we explore the use of multiple sensing technologies to monitor physiological changes that can be used as a proxy for objective measurement of acute pain. Electrodermal activity (EDA), photoplethysmography (PPG), and respiration (RESP) signals were collected from 22 participants under two pain intensities (low and high) and on two different anatomical locations (forearm and hand). Three machine learning models were implemented, including support vector machines (SVM), decision trees (DT), and linear discriminant analysis (LDA) for the identification of pain. Various pain scenarios were investigated, identification of pain (no pain, pain), multiclass (no pain, low pain, high pain), and identification of pain location (forearm, hand). Reference classification results from individual sensors and from all sensors together were obtained. After feature selection, results showed that EDA was the most informative sensor in the three pain conditions, in identification of pain, in the multiclass problem, and for the identification of pain location. These results identify EDA as the superior sensor in our experimental conditions. Future work is required to validate the obtained features to improve its feasibility in more realistic scenarios. Finally, this study proposes EDA as a candidate to design a tool that can assist clinicians in the assessment of acute pain of nonverbal patients.
疼痛评估是临床医生面临的一项具有挑战性的任务。在临床环境中，患者的自我报告被认为是疼痛评估的金标准。然而，无法自我报告疼痛的患者未被诊断出疼痛的风险更高。在本研究中，我们探索使用多种传感技术来监测生理变化，这些变化可作为急性疼痛客观测量的替代指标。从22名参与者的两个疼痛强度（低和高）以及两个不同解剖位置（前臂和手部）收集了皮肤电活动（EDA）、光电容积脉搏波描记法（PPG）和呼吸（RESP）信号。实施了三种机器学习模型，包括支持向量机（SVM）、决策树（DT）和线性判别分析（LDA）用于疼痛识别。研究了各种疼痛情况，包括疼痛识别（无疼痛、疼痛）、多类别（无疼痛、低疼痛、高疼痛）以及疼痛位置识别（前臂、手部）。获得了单个传感器以及所有传感器共同的参考分类结果。经过特征选择，结果表明EDA在三种疼痛条件下、在疼痛识别、多类别问题以及疼痛位置识别方面是信息最丰富的传感器。这些结果确定EDA在我们的实验条件下是 superior传感器。需要开展进一步的工作来验证所获得的特征，以提高其在更现实场景中的可行性。最后，本研究提出将EDA作为一种候选技术来设计一种工具，该工具可协助临床医生评估非语言患者的急性疼痛。
