Bsoul Majdi, Minn Hlaing, Nourani Mehrdad, Gupta Gopal, Tamil Lakshman
Alcatel-Lucent, Plano, TX 75075, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:1178-81. doi: 10.1109/IEMBS.2010.5626011.
Sleep efficiency measures provide an objective assessment to gauge the quality of individual's sleep. In this study we present a home-based, automated and non-intrusive system that is based on Electrocardiogram (ECG) measurements and uses a multi-stage Support Vector Machines (SVM) classifier to measure three indices for sleep quality assessment per 30 s epoch segment: Sleep Efficiency Index, Delta-Sleep Efficiency Index and Sleep Onset Latency. This method provides an alternative to the intrusive and expensive Polysomnography (PSG) and scoring by Rechtschaffen and Kales visual method.
睡眠效率测量提供了一种客观评估方法，用于衡量个人的睡眠质量。在本研究中，我们提出了一种基于家庭的、自动化且非侵入性的系统，该系统基于心电图（ECG）测量，并使用多阶段支持向量机（SVM）分类器，每30秒时间段测量三个睡眠质量评估指标：睡眠效率指数、深度睡眠效率指数和入睡潜伏期。该方法为侵入性且昂贵的多导睡眠图（PSG）以及 Rechtschaffen 和 Kales 的视觉评分方法提供了一种替代方案。
