Postoperative Pain Assessment Model Based on Pulse Contour Characteristics Analysis.

This study aims to develop a new postoperative pain assessment model based on pulse contour analysis and to evaluate its effectiveness in postoperative pain assessment. We derived candidate features from photoplethysmography (PPG) and developed an assessment model based on multiple logistic regression with a combination of features. This study also includes investigations into the optimal unit of analysis and number of features. For model development, PPGs obtained from 78 surgical patients with a six-minute duration in pre- and post-operation conditions, including a training set of 56 pairs and a test set of 22 pairs, were used. In total, 204 candidate features were derived from the spatiotemporal characteristics of the PPG waveform, but 71 features were used without collinearity. We tested models with beat-lengths of 5, 10, 20, 30, 40, 50, 60, 70, and 80, and with 1 to 8 of features for optimization, then determined 30 beats and three features to be the optimal unit of analysis and number of features, respectively. The optimal features were RMSSD-ACV eak/ACA l, AV-A ys, and SD-A , where RMSSD-ACV eak/ACA l is the root mean square of the successive difference of peak amplitude over pulse amplitude from baseline, AV-A ys is the average value of a systolic area of a pulse, and SD-A ys is the standard deviation of a systolic area of a pulse. The accuracy (AC) and area under the curve (AUC) of the proposed model were 0.749 and 0.829 in the development set, respectively, which were superior to those of SPI (AC: 0.643, AUC: 0.716) and ANI (AC: 0.633 AUC: 0.671). In the test set (N=22), the AC and AUC of the proposed model were 0.749 and 0.815, respectively, which were superior to those of SPI (AC: 0.640, AUC: 0.709) and ANI (AC: 0.640, AUC: 0.680).UC: 0.680).