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

Digital Pain Assessment and Management (232) Registered Report (791) Novel Sensors and Data Acquisition Methods in Cardiology (225) Postoperative Pain Management (38) Machine Learning (2076)

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

Authors of this article:

Emad Kasaeyan Naeini1 Author Orcid Image ;   Ajan Subramanian1 Author Orcid Image ;   Michael-David Calderon2 Author Orcid Image ;   Kai Zheng3 Author Orcid Image ;   Nikil Dutt1 Author Orcid Image ;   Pasi Liljeberg4 Author Orcid Image ;   Sanna Salantera5, 6 Author Orcid Image ;   Ariana M Nelson7 Author Orcid Image ;   Amir M Rahmani1, 8, 9 Author Orcid Image
Article Authors Cited by (28) Tweetations (3) Metrics

Journals

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Books/Policy Documents

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Conference Proceedings

  1. Fang R, Zhang R, Hosseini S, Faghih M, Rafatirad S, Rafatirad S, Homayoun H. 2022 4th International Conference on Intelligent Medicine and Image Processing. Pain Level Modeling of Intensive Care Unit patients with Machine Learning Methods: An Effective Congeneric Clustering-based Approach View
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