A preliminary case study: predicting postoperative pain through electrocardiogram

• Main Author: Sebastião, Raquel
• Publication Date: 2022
• Language: eng
• Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Download full: http://hdl.handle.net/10773/37464
• topic: Postoperative pain; ECG; Signal processing; Prediction problems; Machine learning; Decision support

Currently pain is mainly evaluated by resorting to selfreporting instruments, turning the objective evaluation of pain barely impossible. Besides the inherent subjectivity due to these reports, the perception of pain is influenced by several factors. Moreover, cognitive impairments and difficulties in expressing pose a burden difficulty in pain evaluation. Beyond less efficient pain management, the consequences of an incorrect pain assessment may result in over or under dosage of analgesics, with potentially harmful consequences due to the undesirable sideeffects of wrong doses. Therefore, a quantitative and accurate assessment of pain is critical for the adaptation of healthcare strategies, providing a step further in personalized medicine. Thus, the analysis of Autonomic Nervous System (ANS) reactions, which can be assessed continuously with minimally invasive equipment, offers an excellent opportunity to monitor physiological indicators when in the experience of pain. The goal of the proposed work is to classify the presence of pain in postoperative records. The results show accuracy and precision of around 85%, and recall and F1-score of 92%, indicating that the experience of postoperative pain can be classified by relying on physiological data.