Univ Rennes, CHU Rennes, INSERM, LTSI - UMR 1099, F-35000 Rennes, France. Voxygen, F-22560 Pleumeur-Bodou, France.
Physiol Meas. 2019 Feb 26;40(2):02TR02. doi: 10.1088/1361-6579/ab0096.
Video and sound acquisition and processing technologies have seen great improvements in recent decades, with many applications in the biomedical area. The aim of this paper is to review the overall state of the art of advances within these topics in paediatrics and to evaluate their potential application for monitoring in the neonatal intensive care unit (NICU).
For this purpose, more than 150 papers dealing with video and audio processing were reviewed. For both topics, clinical applications are described according to the considered cohorts-full-term newborns, infants and toddlers or preterm newborns. Then, processing methods are presented, in terms of data acquisition, feature extraction and characterization.
The paper first focuses on the exploitation of video recordings; these began to be automatically processed in the 2000s and we show that they have mainly been used to characterize infant motion. Other applications, including respiration and heart rate estimation and facial analysis, are also presented. Audio processing is then reviewed, with a focus on the analysis of crying. The first studies in this field focused on induced-pain cries and the newest ones deal with spontaneous cries; the analyses are mainly based on frequency features. Then, some papers dealing with non-cry signals are also discussed.
Finally, we show that even if recent improvements in digital video and signal processing allow for increased automation of processing, the context of the NICU makes a fully automated analysis of long recordings problematic. A few proposals for overcoming some of the limitations are given.
近几十年来，视频和声音采集及处理技术取得了重大进展，在生物医学领域有诸多应用。本文旨在综述这些领域在儿科领域的最新进展，并评估其在新生儿重症监护病房（NICU）监测中的潜在应用。
为此，我们回顾了 150 多篇涉及视频和音频处理的论文。对于这两个主题，都根据所考虑的队列（足月新生儿、婴儿和学步儿或早产儿）描述了临床应用。然后，从数据采集、特征提取和特征描述等方面介绍了处理方法。
本文首先关注视频记录的利用；这些记录在 21 世纪初开始被自动处理，我们表明它们主要用于描述婴儿的运动特征。还介绍了其他应用，包括呼吸和心率估计以及面部分析。然后回顾了音频处理，重点是对哭声的分析。该领域的早期研究集中在诱发疼痛的哭声上，最新的研究则针对自然哭声；分析主要基于频率特征。然后，还讨论了一些涉及非哭声信号的论文。
最后，我们表明，即使数字视频和信号处理的最新进展允许增加处理的自动化程度，NICU 的背景使得对长时间记录进行完全自动化分析变得困难。给出了一些克服一些限制的建议。
