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arXiv 2003.106012020-03-24cs.CVcs.LGeess.IV

First Investigation Into the Use of Deep Learning for Continuous Assessment of Neonatal Postoperative Pain

University of South Florida（南佛罗里达大学）

Md Sirajus Salekin, Ghada Zamzmi, Dmitry Goldgof, Rangachar Kasturi, Thao Ho, Yu Sun

备注：: Accepted in the 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)
DOI：: 10.1109/FG47880.2020.00082

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This paper presents the first investigation into the use of fully automated deep learning framework for assessing neonatal postoperative pain. It specifically investigates the use of Bilinear Convolutional Neural Network (B-CNN) to extract facial features during different levels of postoperative pain followed by modeling the temporal pattern using Recurrent Neural Network (RNN). Although acute and postoperative pain have some common characteristics (e.g., visual action units), postoperative pain has a different dynamic, and it evolves in a unique pattern over time. Our experimental results indicate a clear difference between the pattern of acute and postoperative pain. They also suggest the efficiency of using a combination of bilinear CNN with RNN model for the continuous assessment of postoperative pain intensity.

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