基于光体积描记光谱图和卷积神经网络的新型术后疼痛评估镇痛指数：观察性研究。
Novel Analgesic Index for Postoperative Pain Assessment Based on a Photoplethysmographic Spectrogram and Convolutional Neural Network: Observational Study.
机构信息
Department of Anaesthesiology and Pain Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Department of Biomedical Engineering, Chonnam National University, Yeosu, Republic of Korea.
出版信息
J Med Internet Res. 2021 Feb 3;23(2):e23920. doi: 10.2196/23920.
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.
背景
虽然基于生物信号处理的商业可用镇痛指数已被用于量化全身麻醉期间的伤害感受，但它们在清醒患者中的性能较低。因此，需要开发一种新的镇痛指数，以提高性能，量化清醒患者的术后疼痛。
目的
本研究旨在开发一种新的镇痛指数，使用光体积描记图（PPG）频谱图和卷积神经网络（CNN）客观评估清醒患者的疼痛。
方法
从一组手术患者中获取 6 分钟的 PPG，既有疼痛（术前）也有无疼痛（术后）。然后，使用后 5 分钟的 PPG 数据进行分析。基于 PPG 和 CNN，我们开发了一种用于疼痛评估的频谱图-CNN 指数。通过计算受试者工作特征曲线下的面积（AUC）来评估这两个指数的性能。
结果
共使用 100 例患者的 PPG 来开发频谱图-CNN 指数。当有疼痛时，频谱图-CNN 指数值显著增加，平均（95%CI）为：基线：28.5（24.2-30.7）与恢复区：65.7（60.5-68.3）；P<.01。AUC 和平衡准确性分别为 0.76 和 71.4%。用于检测疼痛的频谱图-CNN 指数截值为 48，敏感性为 68.3%，特异性为 73.8%。
结论
尽管研究设计存在局限性，但我们证实频谱图-CNN 指数可有效检测清醒患者的术后疼痛。需要进一步研究评估频谱图-CNN 指数的可行性，并防止在包括全身麻醉患者在内的各种人群中过度拟合。
试验注册
临床研究信息服务 KCT0002080；https://cris.nih.go.kr/cris/search/search_result_st01.jsp?seq=6638。
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