TY  - JOUR
AU  - Choi, Byung-Moon
AU  - Yim, Ji Yeon
AU  - Shin, Hangsik
AU  - Noh, Gyujeong
PY  - 2021
DA  - 2021/2/3
TI  - Novel Analgesic Index for Postoperative Pain Assessment Based on a Photoplethysmographic Spectrogram and Convolutional Neural Network: Observational Study
JO  - J Med Internet Res
SP  - e23920
VL  - 23
IS  - 2
KW  - analgesic index
KW  - machine learning
KW  - pain assessment
KW  - photoplethysmogram
KW  - postoperative pain
KW  - spectrogram
AB  - Background: Although commercially available analgesic indices based on biosignal processing have been used to quantify nociception during general anesthesia, their performance is low in conscious patients. Therefore, there is a need to develop a new analgesic index with improved performance to quantify postoperative pain in conscious patients. Objective: This study aimed to develop a new analgesic index using photoplethysmogram (PPG) spectrograms and a convolutional neural network (CNN) to objectively assess pain in conscious patients. Methods: PPGs were obtained from a group of surgical patients for 6 minutes both in the absence (preoperatively) and in the presence (postoperatively) of pain. Then, the PPG data of the latter 5 minutes were used for analysis. Based on the PPGs and a CNN, we developed a spectrogram–CNN index for pain assessment. The area under the curve (AUC) of the receiver-operating characteristic curve was measured to evaluate the performance of the 2 indices. Results: PPGs from 100 patients were used to develop the spectrogram–CNN index. When there was pain, the mean (95% CI) spectrogram–CNN index value increased significantly—baseline: 28.5 (24.2-30.7) versus recovery area: 65.7 (60.5-68.3); P<.01. The AUC and balanced accuracy were 0.76 and 71.4%, respectively. The spectrogram–CNN index cutoff value for detecting pain was 48, with a sensitivity of 68.3% and specificity of 73.8%. Conclusions: Although there were limitations to the study design, we confirmed that the spectrogram–CNN index can efficiently detect postoperative pain in conscious patients. Further studies are required to assess the spectrogram–CNN index’s feasibility and prevent overfitting to various populations, including patients under general anesthesia. Trial Registration: Clinical Research Information Service KCT0002080; https://cris.nih.go.kr/cris/search/search_result_st01.jsp?seq=6638 
SN  - 1438-8871
UR  - http://www.jmir.org/2021/2/e23920/
UR  - https://doi.org/10.2196/23920
UR  - http://www.ncbi.nlm.nih.gov/pubmed/33533723
DO  - 10.2196/23920
ID  - info:doi/10.2196/23920
ER  -