This paper is in the following e-collection/theme issue:

Digital Pain Assessment and Management (233) Machine Learning (2077) New and Emerging Methods and Tools in Surgery and Anesthesiology (54) Perioperative Software Tools for Clinicians (19) Postoperative Pain Management (38)

Published on in Vol 23, No 2 (2021): February

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/23920, first published .
Novel Analgesic Index for Postoperative Pain Assessment Based on a Photoplethysmographic Spectrogram and Convolutional Neural Network: Observational Study

Novel Analgesic Index for Postoperative Pain Assessment Based on a Photoplethysmographic Spectrogram and Convolutional Neural Network: Observational Study

Novel Analgesic Index for Postoperative Pain Assessment Based on a Photoplethysmographic Spectrogram and Convolutional Neural Network: Observational Study

Authors of this article:

Byung-Moon Choi1 Author Orcid Image ;   Ji Yeon Yim2 Author Orcid Image ;   Hangsik Shin2 Author Orcid Image ;   Gyujeong Noh1, 3 Author Orcid Image
Article Authors Cited by (15) Tweetations (2) Metrics

Journals

  1. Khalid S, Ali S, Liu H, Qurashi A, Ali U. Photoplethysmography temporal marker-based machine learning classifier for anesthesia drug detection. Medical & Biological Engineering & Computing 2022;60(11):3057 View
  2. Shin H. Deep convolutional neural network-based signal quality assessment for photoplethysmogram. Computers in Biology and Medicine 2022;145:105430 View
  3. Fernandez Rojas R, Brown N, Waddington G, Goecke R. A systematic review of neurophysiological sensing for the assessment of acute pain. npj Digital Medicine 2023;6(1) View
  4. Fernandez Rojas R, Hirachan N, Brown N, Waddington G, Murtagh L, Seymour B, Goecke R. Multimodal physiological sensing for the assessment of acute pain. Frontiers in Pain Research 2023;4 View
  5. Hashemi S, Yousefzadeh Z, Abin A, Ejmalian A, Nabavi S, Dabbagh A. Machine Learning-Guided Anesthesiology: A Review of Recent Advances and Clinical Applications. Journal of Cellular & Molecular Anesthesia 2024;9(1) View
  6. Pais D, Brás S, Sebastião R. A Review on the Use of Physiological Signals for Assessing Postoperative Pain. ACM Computing Surveys 2025;57(1):1 View
  7. Liao Y, Chen Z, Zhang W, Cheng L, Lin Y, Li P, Zou Z, Zhou M, Li M, Liao C. Artificial intelligence in perioperative pain management: A review. Perioperative Precision Medicine 2024 View
  8. Xie B, Li T, Ma F, Li Q, Xiao Q, Xiong L, Liu F. Artificial intelligence in anesthesiology: a bibliometric analysis. Perioperative Medicine 2024;13(1) View
  9. Bharadwaj A. Revolutionizing perioperative medicine: Technological advancements for enhanced recovery. Serbian Journal of Anesthesia and Intensive Therapy 2025;47(1-2):5 View
  10. Zhang Y, Zha A, Shen W, Dai R. Association of nociception index with postoperative immune status: a prospective observational study. Anesthesiology and Perioperative Science 2025;3(2) View
  11. Byrne M, Cozowicz C, Memtsoudis S, Mariano E, Elkassabany N. Applications of Big Data in Perioperative Outcomes Research and Evidence-Based Clinical Practice. Anesthesiology Clinics 2025;43(4):799 View
  12. Forte A, Avila F, Borna S, Gomez-Cabello C, Pressman S, Haider S, Carter R, Giardi D, Bruce C, McLeod C. Autonomic Parameters Correlated to Acute Postoperative Pain in the Postanesthesia Care Unit: A Systematic Review. Pain Management Nursing 2025 View

Conference Proceedings

  1. Cai R, Bai F, Song S, Zhao D, Liu T, Xu Q. Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences. Study on the relationship between BDNF and electroacupuncture analgesia under the background of Intelligent Technology View
  2. Heynen J, Copot D, Ghita M, Ionescu C. 2021 25th International Conference on System Theory, Control and Computing (ICSTCC). Using convolutional neural network online estimators for predicting pain-level variability enables predictive control of anesthesia View
  3. Gupta A, Saikia T, Gupta P, Dhall A. Companion Proceedings of the 27th International Conference on Multimodal Interaction. PainXtract: A Multimodal System for Multiclass Pain Classification Using Physiological Signals View