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In Journal of medical Internet research ; h5-index 88.0

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.

Choi Byung-Moon, Yim Ji Yeon, Shin Hangsik, Noh Gyujeong

2021-Feb-03

analgesic index, machine learning, pain assessment, photoplethysmogram, postoperative pain, spectrogram