@Article{info:doi/10.2196/23920,
author="Choi, Byung-Moon
and Yim, Ji Yeon
and Shin, Hangsik
and Noh, Gyujeong",
title="Novel Analgesic Index for Postoperative Pain Assessment Based on a Photoplethysmographic Spectrogram and Convolutional Neural Network: Observational Study",
journal="J Med Internet Res",
year="2021",
month="Feb",
day="3",
volume="23",
number="2",
pages="e23920",
keywords="analgesic index; machine learning; pain assessment; photoplethysmogram; postoperative pain; spectrogram",
abstract="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 ",
issn="1438-8871",
doi="10.2196/23920",
url="http://www.jmir.org/2021/2/e23920/",
url="https://doi.org/10.2196/23920",
url="http://www.ncbi.nlm.nih.gov/pubmed/33533723"
}