论文标题
首次研究深度学习持续评估新生儿术后疼痛
First Investigation Into the Use of Deep Learning for Continuous Assessment of Neonatal Postoperative Pain
论文作者
论文摘要
本文提出了对使用全自动深度学习框架评估新生儿术后疼痛的首次研究。它特别研究了双线性卷积神经网络（B-CNN）在不同级别的术后疼痛中提取面部特征，然后使用复发性神经网络（RNN）对时间模式进行建模。尽管急性和术后疼痛具有一些共同的特征（例如，视觉动作单元），但术后疼痛具有不同的动态，并且随着时间的推移会以独特的模式演变。我们的实验结果表明，急性疼痛和术后疼痛的模式之间存在明显的差异。他们还提出了将双线性CNN与RNN模型组合结合的效率，以连续评估术后疼痛强度。
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.
