Long Xi, Foussier Jérôme, Fonseca Pedro, Haakma Reinder, Aarts Ronald M
Annu Int Conf IEEE Eng Med Biol Soc. 2013;2013:5017-20. doi: 10.1109/EMBC.2013.6610675.
In previous work, single-night polysomnography recordings (PSG) of respiratory effort and electrocardiogram (ECG) signals combined with actigraphy were used to classify sleep and wake states. In this study, we aim at classifying rapid-eye-movement (REM) and non-REM (NREM) sleep states. Besides the existing features used for sleep and wake classification, we propose a set of new features based on respiration amplitude. This choice is motivated by the observation that the breathing pattern has a more regular amplitude during NREM sleep than during REM sleep. Experiments were conducted with a data set of 14 healthy subjects using a linear discriminant (LD) classifier. Leave-one-subject-out cross-validations show that adding the new features into the existing feature set results in an increase in Cohen's Kappa coefficient to a value of κ = 0.59 (overall accuracy of 87.6%) compared to that obtained without using these features (κ of 0.54 and overall accuracy of 86.4%). In addition, we compared the results to those reported in some other studies with different features and signal modalities.
在之前的研究中，通过结合呼吸努力和心电图（ECG）信号的单夜多导睡眠图记录（PSG）以及活动记录仪来对睡眠和清醒状态进行分类。在本研究中，我们旨在对快速眼动（REM）和非快速眼动（NREM）睡眠状态进行分类。除了用于睡眠和清醒分类的现有特征外，我们还基于呼吸幅度提出了一组新的特征。做出这一选择的动机在于观察到，与REM睡眠期间相比，NREM睡眠期间的呼吸模式具有更规则的幅度。使用线性判别（LD）分类器对14名健康受试者的数据集进行了实验。留一受试者交叉验证表明，与不使用这些特征时相比（κ为0.54，总体准确率为86.4%），将新特征添加到现有特征集中会使科恩kappa系数增加到κ = 0.59（总体准确率为87.6%）。此外，我们将结果与其他一些使用不同特征和信号模式的研究报告结果进行了比较。
