使用单导联心电图和多阶段支持向量机分类器进行实时睡眠质量评估。
Real-time sleep quality assessment using single-lead ECG and multi-stage SVM classifier.
作者信息
Bsoul Majdi, Minn Hlaing, Nourani Mehrdad, Gupta Gopal, Tamil Lakshman
机构信息
Alcatel-Lucent, Plano, TX 75075, USA.
出版信息
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。
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