Pain Monitoring Using Heart Rate Variability and Photoplethysmograph-Derived Parameters by Binary Logistic Regression

Abstract Purpose To construct a pain classification model using binary logistic regression to calculate probability and monitor based on heart rate variability (HRV) photoplethysmography (PPG) parameters. Methods Heat stimulation was used simulate for modeling the generation process, electrocardiography PPG signals were recorded simultaneously. After signal analysis, statistical analysis performed SPSS determine parameters that significant pain. Thereafter, with HRV established regression. Results The sensitivity specificity of 60.0% 72.0%, respectively. When occurred, calculated increased from < 50% > 50%. relieved, decreased consistent numeric rating scale value, which indicated can correctly presence Conclusion This has sufficient robustness adaptability be applied different healthy people monitoring. is helpful in establishing real-time monitoring system improve management patients postoperative intensive care unit patient-controlled analgesia provide reference doctors regarding medication.