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

Books/Policy Documents

  1. Bieńkowska M, Badura A, Myśliwiec A, Pietka E. Information Technology in Biomedicine. View