Sazonova Nadezhda A, Sazonov Edward E, Tan Bozhao, Schuckers Stephanie A C
Dept. of Comput. Sci. & Electr. Eng., West Virginia Univ., Morgantown, WV 26506, USA.
Conf Proc IEEE Eng Med Biol Soc. 2006;2006:2462-5. doi: 10.1109/IEMBS.2006.259719.
Sleep state scoring usually relies on polysomnographic measurements, which include electroencephalogram (EEG), electromyogram (EMG), electro-oculogram (EOG), two or three lead chest electrocardiogram (ECG), and may include other measurements. Overall, polysomnography is an intrusive procedure not well tolerated by infants and elderly. The goal of this research is to study possibility of automatic sleep state scoring from less intrusive measurements such as activity measurements and respiratory measurements by inductive plethysmography. The study is based on the Collaborative Home Infant Monitoring Evaluation (CHIME) dataset. Results demonstrate that the suggested approach is capable of scoring sleep states (awake, rapid eye movement and quiet sleep) with good accuracy.
睡眠状态评分通常依赖于多导睡眠图测量，其中包括脑电图（EEG）、肌电图（EMG）、眼电图（EOG）、两导联或三导联胸部心电图（ECG），并且可能包括其他测量。总体而言，多导睡眠图是一种侵入性检查，婴儿和老年人对此耐受性不佳。本研究的目的是通过诸如活动测量和感应式体积描记法进行的呼吸测量等侵入性较小的测量来研究自动睡眠状态评分的可能性。该研究基于协作式家庭婴儿监测评估（CHIME）数据集。结果表明，所提出的方法能够以良好的准确性对睡眠状态（清醒、快速眼动和安静睡眠）进行评分。
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