Observational studyJournal of medical Internet research2021
Novel Analgesic Index for Postoperative Pain Assessment Based on a Photoplethysmographic Spectrogram and Convolutional Neural Network: Observational Study.
Observational study in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 18 citations in OpenAlex.
- Towards Intelligent Pain Monitoring Systems: A Survey of Recent Technologies and Methods.Sensors (Basel, Switzerland) · 2026Review
- Pain assessment using physiological responses/markers in different types of pain: a scoping review.NPJ digital medicine · 2026Article
- Predictive machine learning for postoperative pain using biosignals: a retrospective observational study.BMC medical informatics and decision making · 2025Observational
- Artificial intelligence in anesthesiology: a bibliometric analysis.Perioperative medicine (London, England) · 2024Review
- A systematic review of neurophysiological sensing for the assessment of acute pain.NPJ digital medicine · 2023Review
- Multimodal physiological sensing for the assessment of acute pain.Frontiers in pain research (Lausanne, Switzerland) · 2023Article
- Photoplethysmography temporal marker-based machine learning classifier for anesthesia drug detection.Medical & biological engineering & computing · 2022Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAlthough 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.
objectiveThis 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.
methodsPPGs 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.
resultsPPGs 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%.
conclusionsAlthough 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 registrationClinical Research Information Service KCT0002080; https://cris.nih.go.kr/cris/search/search_result_st01.jsp?seq=6638.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.