Postoperative Pain Assessment Model Based on Pulse Contour Characteristics Analysis

Area of Science:

• Biomedical Engineering
• Physiological Monitoring
• Pain Assessment

Background:

• Postoperative pain management requires accurate assessment.
• Current pain assessment methods have limitations.
• Photoplethysmography (PPG) offers a potential non-invasive monitoring solution.

Purpose of the Study:

• To develop and validate a novel postoperative pain assessment model.
• To utilize pulse contour analysis of PPG signals.
• To evaluate the model's effectiveness compared to existing methods.

Main Methods:

• Derived candidate features from PPG signals in 78 surgical patients.
• Developed a logistic regression model using optimized features (20 beats, 3 features).
• Evaluated model performance using accuracy (AC) and area under the curve (AUC).

Main Results:

• The proposed model achieved high AC (0.793) and AUC (0.872) in the development set.
• The model demonstrated superior performance in the test set (AC: 0.712, AUC: 0.808).
• Outperformed existing pain assessment indices (SPI and ANI) in both sets.

Conclusions:

• A novel, non-invasive postoperative pain assessment model based on PPG pulse contour analysis was successfully developed.
• The model shows significant potential for accurate and effective pain evaluation.
• This approach offers a superior alternative to current pain monitoring techniques.