IEEE J Biomed Health Inform. 2020 Nov;24(11):3258-3267. doi: 10.1109/JBHI.2020.2978252. Epub 2020 Nov 4.
Birth asphyxia is one of the leading causes of neonatal deaths. A key for survival is performing immediate and continuous quality newborn resuscitation. A dataset of recorded signals during newborn resuscitation, including videos, has been collected in Haydom, Tanzania, and the aim is to analyze the treatment and its effect on the newborn outcome. An important step is to generate timelines of relevant resuscitation activities, including ventilation, stimulation, suction, etc., during the resuscitation episodes.
We propose a two-step deep neural network system, ORAA-net, utilizing low-quality video recordings of resuscitation episodes to do activity recognition during newborn resuscitation. The first step is to detect and track relevant objects using Convolutional Neural Networks (CNN) and post-processing, and the second step is to analyze the proposed activity regions from step 1 to do activity recognition using 3D CNNs.
The system recognized the activities newborn uncovered, stimulation, ventilation and suction with a mean precision of 77.67%, a mean recall of 77,64%, and a mean accuracy of 92.40%. Moreover, the accuracy of the estimated number of Health Care Providers (HCPs) present during the resuscitation episodes was 68.32%.
The results indicate that the proposed CNN-based two-step ORAA-net could be used for object detection and activity recognition in noisy low-quality newborn resuscitation videos.
A thorough analysis of the effect the different resuscitation activities have on the newborn outcome could potentially allow us to optimize treatment guidelines, training, debriefing, and local quality improvement in newborn resuscitation.
出生窒息是新生儿死亡的主要原因之一。生存的关键是立即进行持续的高质量新生儿复苏。坦桑尼亚海顿收集了新生儿复苏过程中记录的信号数据集，包括视频，目的是分析治疗方法及其对新生儿结局的影响。一个重要步骤是生成复苏过程中相关复苏活动的时间表，包括通气、刺激、抽吸等。
我们提出了一种两步深度神经网络系统 ORAA-net，利用新生儿复苏过程中低质量的视频记录进行活动识别。第一步是使用卷积神经网络（CNN）和后处理检测和跟踪相关对象，第二步是从第一步分析所提出的活动区域，使用 3D CNN 进行活动识别。
该系统识别出新生儿暴露、刺激、通气和抽吸等活动的平均精度为 77.67%，平均召回率为 77.64%，平均准确率为 92.40%。此外，估计复苏过程中存在的卫生保健提供者（HCP）数量的准确性为 68.32%。
结果表明，基于 CNN 的两步 ORAA-net 可用于嘈杂的低质量新生儿复苏视频中的目标检测和活动识别。
对不同复苏活动对新生儿结局的影响进行深入分析，可能使我们能够优化治疗指南、培训、汇报和新生儿复苏的本地质量改进。
