Long Xi, Fonseca Pedro, Haakma Reinder, Foussier Jérôme, Aarts Ronald M
Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:50-3. doi: 10.1109/EMBC.2014.6943526.
This preliminary study investigated the use of cardiac information or more specifically, heart rate variability (HRV), for automatic deep sleep detection throughout the night. The HRV data can be derived from cardiac signals, which were obtained from polysomnography (PSG) recordings. In total 42 features were extracted from the HRV data of 15 single-night PSG recordings (from 15 healthy subjects) for each 30-s epoch, used to perform epoch-by-epoch classification of deep sleep and non-deep sleep (including wake state and all the other sleep stages except deep sleep). To reduce variation of cardiac physiology between subjects, we normalized each feature per subject using a simple Z-score normalization method by subtracting the mean and dividing by the standard deviation of the feature values. A correlation-based feature selection (CFS) method was employed to select informative features as well as removing feature redundancy and a linear discriminant (LD) classifier was applied for deep and non-deep sleep classification. Results show that the use of Z-score normalization can significantly improve the classification performance. A Cohen's Kappa coefficient of 0.42 and an overall accuracy of 81.3% based on a leave-one-subject-out cross-validation were achieved.
这项初步研究调查了利用心脏信息，或者更具体地说，心率变异性（HRV）来进行整夜自动深度睡眠检测的情况。HRV数据可从心脏信号中获取，这些信号来自多导睡眠图（PSG）记录。对于每个30秒的时段，从15个单夜PSG记录（来自15名健康受试者）的HRV数据中总共提取了42个特征，用于逐时段对深度睡眠和非深度睡眠（包括清醒状态以及除深度睡眠外的所有其他睡眠阶段）进行分类。为了减少受试者之间心脏生理的差异，我们使用简单的Z分数归一化方法对每个受试者的每个特征进行归一化，即减去特征值的均值并除以标准差。采用基于相关性的特征选择（CFS）方法来选择信息性特征并去除特征冗余，并应用线性判别（LD）分类器进行深度睡眠和非深度睡眠分类。结果表明，使用Z分数归一化可以显著提高分类性能。基于留一受试者交叉验证，获得了0.42的科恩卡帕系数和81.3%的总体准确率。
