Published on in Vol 23, No 5 (2021): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/25079, first published .
Pain Recognition With Electrocardiographic Features in Postoperative Patients: Method Validation Study

Pain Recognition With Electrocardiographic Features in Postoperative Patients: Method Validation Study

Pain Recognition With Electrocardiographic Features in Postoperative Patients: Method Validation Study

Journals

  1. Sadik O, Schaffer J, Land W, Xue H, Yazgan I, Kafesçiler K, Sungur M. A Bayesian Network Concept for Pain Assessment. JMIR Biomedical Engineering 2022;7(2):e35711 View
  2. Winslow B, Kwasinski R, Whirlow K, Mills E, Hullfish J, Carroll M. Automatic detection of pain using machine learning. Frontiers in Pain Research 2022;3 View
  3. Kutafina E, Becker S, Namer B. Measuring pain and nociception: Through the glasses of a computational scientist. Transdisciplinary overview of methods. Frontiers in Network Physiology 2023;3 View
  4. Somani S, Yu K, Chiu A, Sykes K, Villwock J. Consumer Wearables for Patient Monitoring in Otolaryngology: A State of the Art Review. Otolaryngology–Head and Neck Surgery 2022;167(4):620 View
  5. Liang W, Fan Y. Deep Learning-Based ECG Abnormality Identification Prediction and Analysis. Journal of Sensors 2022;2022:1 View
  6. 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
  7. Alostad H, Dawiek S, Davulcu H. Q8VaxStance: Dataset Labeling System for Stance Detection towards Vaccines in Kuwaiti Dialect. Big Data and Cognitive Computing 2023;7(3):151 View
  8. Dudarev V, Barral O, Zhang C, Davis G, Enns J. On the Reliability of Wearable Technology: A Tutorial on Measuring Heart Rate and Heart Rate Variability in the Wild. Sensors 2023;23(13):5863 View
  9. Zhu W, Liu C, Yu H, Guo Y, Xiao Y, Lin Y. COMPASS App: A Patient-centered Physiological based Pain Assessment System. Proceedings of the Human Factors and Ergonomics Society Annual Meeting 2023;67(1):1361 View
  10. Lu Z, Ozek B, Kamarthi S. Transformer encoder with multiscale deep learning for pain classification using physiological signals. Frontiers in Physiology 2023;14 View
  11. Wang H, Wang Q, He Q, Li S, Zhao Y, Zuo Y. Current perioperative nociception monitoring and potential directions. Asian Journal of Surgery 2024;47(6):2558 View
  12. Albahdal D, Aljebreen W, Ibrahim D. PainMeter: Automatic Assessment of Pain Intensity Levels From Multiple Physiological Signals Using Machine Learning. IEEE Access 2024;12:48349 View
  13. 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
  14. Subramanian A, Cao R, Naeni E, Aqajari S, Hughes T, Calderon M, Zheng K, Dutt N, Liljeberg P, Salanterä S, Nelson A, Rahmani A. Multimodal Pain Recognition in Postoperative Patients: A Machine Learning Approach (Preprint). JMIR Formative Research 2024 View

Books/Policy Documents

  1. Bieńkowska M, Badura A, Myśliwiec A, Pietka E. Information Technology in Biomedicine. View
  2. Pais D, Sebastião R. Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. View
  3. Ma Y, Wu X, Wang X, Li J, Qin P, Yin M, Cao W, Yi Z. Cognitive Computation and Systems. View