IEEE Rev Biomed Eng. 2018;11:77-96. doi: 10.1109/RBME.2017.2777907. Epub 2017 Nov 27.
Bedside caregivers assess infants' pain at constant intervals by observing specific behavioral and physiological signs of pain. This standard has two main limitations. The first limitation is the intermittent assessment of pain, which might lead to missing pain when the infants are left unattended. Second, it is inconsistent since it depends on the observer's subjective judgment and differs between observers. Intermittent and inconsistent assessment can induce poor treatment and, therefore, cause serious long-term consequences. To mitigate these limitations, the current standard can be augmented by an automated system that monitors infants continuously and provides quantitative and consistent assessment of pain. Several automated methods have been introduced to assess infants' pain automatically based on analysis of behavioral or physiological pain indicators. This paper comprehensively reviews the automated approaches (i.e., approaches to feature extraction) for analyzing infants' pain and the current efforts in automatic pain recognition. In addition, it reviews the databases available to the research community and discusses the current limitations of the automated pain assessment.
床边护理人员通过观察婴儿疼痛的特定行为和生理迹象,在固定时间间隔评估婴儿的疼痛。这种标准有两个主要的局限性。第一个局限性是疼痛的间歇性评估,这可能导致在婴儿无人看管时错过疼痛。其次,它是不一致的,因为它取决于观察者的主观判断,并且在观察者之间存在差异。间歇性和不一致的评估可能导致治疗不佳,并因此造成严重的长期后果。为了减轻这些局限性,可以通过连续监测婴儿并提供疼痛的定量和一致评估的自动化系统来增强当前标准。已经引入了几种自动方法来基于对行为或生理疼痛指标的分析来自动评估婴儿的疼痛。本文全面回顾了用于分析婴儿疼痛的自动化方法(即特征提取方法)以及自动疼痛识别的当前进展。此外,它还回顾了可供研究界使用的数据库,并讨论了自动疼痛评估的当前局限性。