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首页> 外文会议>2018 3rd International Conference on Circuits, Control, Communication and Computing >Cloud-Based Framework for Pain Scale Assessment in NICU- A Primitive Study with Infant Cries
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Cloud-Based Framework for Pain Scale Assessment in NICU- A Primitive Study with Infant Cries

机译：基于云的新生儿重症监护病房疼痛量表评估框架-婴幼儿啼哭的原始研究

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Pain scale assessment in a typical neonatal intensive care unit (NICU) is quite challenging for the Neonatology Clinical community. Infant cries, a mere indicator of pain has been considered an effective pathological tool to assess the status of the newborns. Especially in a resource-constrained setting, newborn mortality rate is growing exponentially due to lack of facilities and assessment by the specialist. This specific study attempts to propose a cloud-based framework with the quantitative annotation that will connect the clinician, caretaker and the staff nurse to view the current condition of the newborn. Three variants of cry recording hunger, pain and discomfort were used for this primitive study. Initially, a graphical user interface (GUI) was developed to assess and classify the crying pattern. Power spectral feature and Support Vector Machine (SVM) based neural network were considered for pattern classification. Later using the thingspeak IOT cloud platform, communication was established to clinician/caretaker through the mobile device. A centralized station with the cloud framework facility is currently under development. This primitive study thus enhances the cyber-physical structure to assess the pain scale of newborns and will help in reducing the mortality rate.
机译：对于新生儿科临床社区而言，在典型的新生儿重症监护病房（NICU）中进行疼痛量表评估非常具有挑战性。婴儿的哭声只是疼痛的一种指标，被认为是评估新生儿状况的有效病理工具。特别是在资源有限的情况下，由于缺乏设施和专家的评估，新生儿死亡率呈指数级增长。这项特定的研究试图提出一个基于云的框架，并带有定量注释，该注释将连接临床医生，看护人和医护人员以查看新生儿的当前状况。这项记录了饥饿，疼痛和不适感的哭声的三种变体被用于这项原始研究。最初，开发了图形用户界面（GUI）来评估和分类哭泣模式。模式分类考虑了基于功率谱特征和基于支持向量机（SVM）的神经网络。后来使用Thingspeak物联网云平台，通过移动设备与临床医生/看护人建立了通信。目前正在开发具有云框架功能的集中式工作站。因此，这项原始研究增强了网络物理结构，以评估新生儿的疼痛程度，并有助于降低死亡率。

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