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](https://asset.jmir.pub/assets/11da4e3c23270a3768f3cf534b142a2e.png 480w,https://asset.jmir.pub/assets/11da4e3c23270a3768f3cf534b142a2e.png 960w,https://asset.jmir.pub/assets/11da4e3c23270a3768f3cf534b142a2e.png 1920w,https://asset.jmir.pub/assets/11da4e3c23270a3768f3cf534b142a2e.png 2500w)
Journals
- 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
- Shin H. Deep convolutional neural network-based signal quality assessment for photoplethysmogram. Computers in Biology and Medicine 2022;145:105430 View
- 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
- 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
- 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