Schwichtenberg A J, Choe Jeehyun, Kellerman Ashleigh, Abel Emily A, Delp Edward J
Department of Human Development and Family Studies, College of Health and Human Sciences, Purdue University, West Lafayette, IN, United States.
School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, United States.
Front Pediatr. 2018 Jun 19;6:158. doi: 10.3389/fped.2018.00158. eCollection 2018.
The term videosomnography captures a range of video-based methods used to record and subsequently score sleep behaviors (most commonly sleep vs. wake states). Until recently, the time consuming nature of behavioral videosomnography coding has limited its clinical and research applications. However, with recent technological advancements, the use of auto-videosomnography techniques may be a practical and valuable extension of behavioral videosomnography coding. To test an auto-videosomnography system within a pediatric sample, we processed 30 videos of infant/toddler sleep using a series of signal/video-processing techniques. The resulting auto-videosomnography system provided minute-by-minute sleep vs. wake estimates, which were then compared to behaviorally coded videosomnography and actigraphy. Minute-by-minute estimates demonstrated moderate agreement across compared methods (auto-videosomnography with behavioral videosomnography, Cohen's kappa = 0.46; with actigraphy = 0.41). Additionally, auto-videosomnography agreements exhibited high sensitivity for sleep but only about half of the wake minutes were correctly identified. For sleep timing (sleep onset and morning rise time), behavioral videosomnography and auto-videosomnography demonstrated strong agreement. However, nighttime waking agreements were poor across both behavioral videosomnography and actigraphy comparisons. Overall, this study provides preliminary support for the use of an auto-videosomnography system to index sleep onset and morning rise time only, which may have potential telemedicine implications. With replication, auto-videosomnography may be useful for researchers and clinicians as a minimally invasive sleep timing assessment method.
视频睡眠监测这一术语涵盖了一系列用于记录并随后对睡眠行为（最常见的是睡眠与清醒状态）进行评分的基于视频的方法。直到最近，行为视频睡眠监测编码的耗时特性限制了其临床和研究应用。然而，随着最近的技术进步，自动视频睡眠监测技术的使用可能是行为视频睡眠监测编码的一种实用且有价值的扩展。为了在儿科样本中测试自动视频睡眠监测系统，我们使用一系列信号/视频处理技术处理了30段婴幼儿睡眠视频。由此产生的自动视频睡眠监测系统提供了逐分钟的睡眠与清醒估计值，然后将其与行为编码的视频睡眠监测和活动记录仪进行比较。逐分钟估计值在比较的方法之间显示出中等程度的一致性（自动视频睡眠监测与行为视频睡眠监测，科恩kappa系数 = 0.46；与活动记录仪 = 0.41）。此外，自动视频睡眠监测的一致性对睡眠表现出高敏感性，但只有大约一半的清醒分钟被正确识别。对于睡眠时间（入睡时间和早晨起床时间），行为视频睡眠监测和自动视频睡眠监测显示出很强的一致性。然而，在行为视频睡眠监测和活动记录仪比较中，夜间觉醒的一致性都很差。总体而言，本研究为仅使用自动视频睡眠监测系统来确定入睡时间和早晨起床时间提供了初步支持，这可能具有潜在的远程医疗意义。通过重复验证，自动视频睡眠监测可能作为一种微创的睡眠时间评估方法，对研究人员和临床医生有用。
