Department of Neurosciences, Catholic University, Rome, Italy.
Clin Neurophysiol. 2012 Feb;123(2):318-23. doi: 10.1016/j.clinph.2011.07.026. Epub 2011 Aug 27.
The aim of the present study was to develop and validate a software tool for the detection of movements during sleep, based on the automated analysis of video recordings. This software is aimed to detect and quantify movements and to evaluate periods of sleep and wake.
We applied an open-source software, previously distributed on the web (Zoneminder, ZM), meant for video surveillance. A validation study was performed: computed movement analysis was compared with two standardised, 'gold standard' methods for the analysis of sleep-wake cycles: actigraphy and laboratory-based video-polysomnography.
Sleep variables evaluated by ZM were not different from those measured by traditional sleep-scoring systems. Bland-Altman plots showed an overlap between the scores obtained with ZM, PSG and actigraphy, with a slight tendency of ZM to overestimate nocturnal awakenings. ZM showed a good degree of accuracy both with respect to PSG (79.9%) and actigraphy (83.1%); and had very high sensitivity (ZM vs. PSG: 90.4%; ZM vs. actigraphy: 89.5%) and relatively lower specificity (ZM vs. PSG: 42.3%; ZM vs. actigraphy: 65.4%).
The computer-assisted motion analysis is reliable and reproducible, and it can allow a reliable esteem of some sleep and wake parameters. The motion-based sleep analysis shows a trend to overestimate wakefulness.
The possibility to measure sleep from video recordings may be useful in those clinical and experimental conditions in which traditional PSG studies may not be performed.
本研究旨在开发和验证一种基于视频记录自动分析的睡眠期间运动检测软件工具。该软件旨在检测和量化运动，并评估睡眠和清醒期。
我们应用了一种开源软件，该软件先前在网络上发布（Zoneminder，ZM），用于视频监控。进行了验证研究：计算运动分析与两种用于睡眠-觉醒周期分析的标准化“金标准”方法（活动记录仪和基于实验室的视频多导睡眠图）进行了比较。
ZM 评估的睡眠变量与传统睡眠评分系统测量的睡眠变量没有差异。Bland-Altman 图显示，ZM、PSG 和活动记录仪获得的评分之间存在重叠，ZM 略微倾向于高估夜间觉醒次数。ZM 在 PSG（79.9%）和活动记录仪（83.1%）方面具有较高的准确性，并且具有非常高的敏感性（ZM 与 PSG：90.4%；ZM 与活动记录仪：89.5%）和相对较低的特异性（ZM 与 PSG：42.3%；ZM 与活动记录仪：65.4%）。
计算机辅助运动分析可靠且可重复，并且可以可靠估计某些睡眠和清醒参数。基于运动的睡眠分析显示出高估清醒的趋势。
从视频记录中测量睡眠的可能性可能在那些传统 PSG 研究无法进行的临床和实验条件下有用。
