Automatic sleep staging based on ECG signals using hidden Markov models
Automatic sleep staging based on ECG signals using hidden Markov models
复制标题
使用隐马尔可夫模型根据心电图信号进行自动睡眠分期
DOI：
10.1109/embc.2015.7318416
复制
发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Wenxi Chen
中科院分区：
文献类型：
--
作者：
Ying Chen;Xin Zhu;Wenxi Chen
这项研究的目的是研究使用仅来自心电信号的特征进行自动睡眠分期的可行性。研究是使用隐马尔可夫模型(HMM)框架进行的。以每个30秒时段计算的心率(HR)的平均值和SD值为特征。首先利用集成经验模式分解(EEMD)对两个特征序列进行去趋势化，形成二维特征向量，然后利用矢量量化(VQ)方法将其转换为编码向量。利用输出VQ指数估计隐马尔可夫模型的参数。所提出的模型在一组健康个体上用留一法交叉验证进行了测试和评估。将自动睡眠分期结果与PSG估计值进行比较。结果显示，深度睡眠、浅睡眠、快速眼动睡眠和觉醒睡眠的准确率分别为82.2%、76.0%、76.1%和85.5%。研究结果证明，基于HRS的HMM方法用于自动睡眠分期是可行的，可以为开发更高效、更健壮、更简单的适合家庭应用的睡眠分期系统铺平道路。
This study is designed to investigate the feasibility of automatic sleep staging using features only derived from electrocardiography (ECG) signal. The study was carried out using the framework of hidden Markov models (HMMs). The mean, and SD values of heart rates (HRs) computed from each 30-second epoch served as the features. The two feature sequences were first detrended by ensemble empirical mode decomposition (EEMD), formed as a two-dimensional feature vector, and then converted into code vectors by vector quantization (VQ) method. The output VQ indexes were utilized to estimate parameters for HMMs. The proposed model was tested and evaluated on a group of healthy individuals using leave-one-out cross-validation. The automatic sleep staging results were compared with PSG estimated ones. Results showed accuracies of 82.2%, 76.0%, 76.1% and 85.5% for deep, light, REM and wake sleep, respectively. The findings proved that HRs-based HMM approach is feasible for automatic sleep staging and can pave a way for developing more efficient, robust, and simple sleep staging system suitable for home application.