Keller James M, Popescu Mihail, Skubic Marjorie
Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug;2016:2843-2846. doi: 10.1109/EMBC.2016.7591322.
This paper presents a sleep stage recognition system for Awake, rapid eye movement (REM) and non-REM (NREM) sleep detection. Two respiratory variability (RV) features are extracted from oro-nasal airflow signals provided in the sleep-EDF (Expanded) database. A two layer system with threshold comparison classifier is implemented. This system achieved state-of-the-art performance with simple features and classifiers. The average accuracy of 74.00%±5.30% and Cohen's kappa coefficient of 0.49±0.08 were achieved with 21 recordings. In the end, the measure of sleep efficiency was calculated and the average absolute error was 3.61%±3.66%.
本文提出了一种用于清醒、快速眼动（REM）和非快速眼动（NREM）睡眠检测的睡眠阶段识别系统。从睡眠-EDF（扩展）数据库中提供的口鼻气流信号中提取了两种呼吸变异性（RV）特征。实现了一个带有阈值比较分类器的两层系统。该系统通过简单的特征和分类器实现了先进的性能。21次记录的平均准确率为74.00%±5.30%，科恩卡帕系数为0.49±0.08。最后，计算了睡眠效率指标，平均绝对误差为3.61%±3.66%。
