Objective: 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.
Methods: 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.
Results: 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%).
Conclusions: 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.
Significance: 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.
Copyright © 2011 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.