Byung-Moon Choi 1 , Ji Yeon Yim 2 , Hangsik Shin 2 , Gyujeong Noh 1,3 Show Affiliations »
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. ©Byung-Moon Choi, Ji Yeon Yim, Hangsik Shin, Gyujeong Noh. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 03.02.2021.
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. ©Byung-Moon Choi, Ji Yeon Yim, Hangsik Shin, Gyujeong Noh. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 03.02.2021.
Entities: CellLine
Chemical
Disease
Gene
Species
Keywords:
analgesic index; machine learning; pain assessment; photoplethysmogram; postoperative pain; spectrogram
Year: 2021
PMID： 33533723 PMCID： PMC7889419 DOI： 10.2196/23920
Source DB: PubMed Journal: J Med Internet Res ISSN： 1438-8871 Impact factor: 5.428