Majeedi Abrar, McAdams Ryan M, Kaur Ravneet, Gupta Shubham, Singh Harpreet, Li Yin
Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA.
Department of Pediatrics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA.
NPJ Digit Med. 2024 Jun 27;7(1):172. doi: 10.1038/s41746-024-01164-y.
Early-life exposure to stress results in significantly increased risk of neurodevelopmental impairments with potential long-term effects into childhood and even adulthood. As a crucial step towards monitoring neonatal stress in neonatal intensive care units (NICUs), our study aims to quantify the duration, frequency, and physiological responses of care manipulation activities, based on bedside videos and physiological signals. Leveraging 289 h of video recordings and physiological data within 330 sessions collected from 27 neonates in 2 NICUs, we develop and evaluate a deep learning method to detect manipulation activities from the video, to estimate their duration and frequency, and to further integrate physiological signals for assessing their responses. With a 13.8% relative error tolerance for activity duration and frequency, our results were statistically equivalent to human annotations. Further, our method proved effective for estimating short-term physiological responses, for detecting activities with marked physiological deviations, and for quantifying the neonatal infant stressor scale scores.
生命早期暴露于应激会显著增加神经发育障碍的风险，并可能对儿童期甚至成年期产生长期影响。作为在新生儿重症监护病房（NICU）监测新生儿应激的关键一步，我们的研究旨在基于床边视频和生理信号，量化护理操作活动的持续时间、频率和生理反应。利用从2个NICU的27名新生儿收集的330次会话中的289小时视频记录和生理数据，我们开发并评估了一种深度学习方法，用于从视频中检测操作活动，估计其持续时间和频率，并进一步整合生理信号以评估其反应。对于活动持续时间和频率，我们的结果具有13.8%的相对误差容忍度，在统计学上与人工标注相当。此外，我们的方法被证明在估计短期生理反应、检测具有明显生理偏差的活动以及量化新生儿应激源量表得分方面是有效的。
