Multimodal spatio-temporal deep learning approach for neonatal postoperative pain assessment.
Multimodal spatio-temporal deep learning approach for neonatal postoperative pain assessment.
复制标题
新生儿术后疼痛评估的多模式时空深度学习方法。
DOI：
10.1016/j.compbiomed.2020.104150
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发表时间：
2021-03
影响因子：
7.7
通讯作者：
Sun Y
中科院分区：
文献类型：
--
作者：
Salekin MS;Zamzmi G;Goldgof D;Kasturi R;Ho T;Sun Y
目前评估新生儿术后疼痛的做法依赖于床边护理人员。这种做法是主观的、不一致的、缓慢的和不连续的。为了开发可靠的医学解释，已经提出了几种自动化方法来增强当前的实践。这些方法是单峰的，主要集中在评估新生儿程序性(急性)疼痛。由于疼痛是一种经常通过多种方式表达的多模式情绪，因此对疼痛的多模式评估是必要的，特别是在术后(急性持续的)疼痛的情况下。此外，时空分析随着时间的推移更加稳定，并已被证明在最小化错误分类错误方面非常有效。在本文中，我们提出了一种新的多模式时空方法，整合了视觉和声音信号，并使用它们来评估新生儿术后疼痛。我们进行了全面的实验来考察所提出的方法的有效性。我们比较了多模式和单模式术后疼痛评估的性能，并测量了时间信息整合的影响。在真实数据集上的实验结果表明，多模式时空方法获得了最高的AUC(0.87)和准确率(79%)，分别比单峰方法平均提高6.67%和6.33%。结果还表明，与非时态方法相比，时间信息的整合显著提高了性能，因为它捕捉到了疼痛动态的变化。这些结果表明，所提出的方法可以作为人工评估的一种可行的替代方案，人工评估将在临床环境、护理点测试和家庭中走向全自动疼痛监测的道路。
The current practice for assessing neonatal postoperative pain relies on bedside caregivers. This practice is subjective, inconsistent, slow, and discontinuous. To develop a reliable medical interpretation, several automated approaches have been proposed to enhance the current practice. These approaches are unimodal and focus mainly on assessing neonatal procedural (acute) pain. As pain is a multimodal emotion that is often expressed through multiple modalities, the multimodal assessment of pain is necessary especially in case of postoperative (acute prolonged) pain. Additionally, spatio-temporal analysis is more stable over time and has been proven to be highly effective at minimizing misclassification errors. In this paper, we present a novel multimodal spatio-temporal approach that integrates visual and vocal signals and uses them for assessing neonatal postoperative pain. We conduct comprehensive experiments to investigate the effectiveness of the proposed approach. We compare the performance of the multimodal and unimodal postoperative pain assessment, and measure the impact of temporal information integration. The experimental results, on a real-world dataset, show that the proposed multimodal spatio-temporal approach achieves the highest AUC (0.87) and accuracy (79%), which are on average 6.67% and 6.33% higher than unimodal approaches. The results also show that the integration of temporal information markedly improves the performance as compared to the nontemporal approach as it captures changes in the pain dynamic. These results demonstrate that the proposed approach can be used as a viable alternative to manual assessment, which would tread a path toward fully automated pain monitoring in clinical settings, point-of-care testing, and homes.
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[新生儿中的疼痛评估量表：综合评论]。
影响因子：
1.7
作者：
de Melo, Gleicia Martins;Lelis, Ana Luiza Paula de Aguiar;de Moura, Alline Falconieri;Cardoso, Maria Vera Lucia Moreira Leitao;da Silva, Viviane Martins
通讯作者：
da Silva, Viviane Martins
影响因子：
3.8
作者：
Koul A;Becchio C;Cavallo A
通讯作者：
Cavallo A
影响因子：
2.9
作者：
Hummel, P.;Puchalski, M.;Weiss, M. G.
通讯作者：
Weiss, M. G.
影响因子：
7.7
作者：
Fortier MA;Chung WW;Martinez A;Gago-Masague S;Sender L
通讯作者：
Sender L
影响因子：
2.7
作者：
Gan TJ
通讯作者：
Gan TJ