Sleep stage classification with ECG and respiratory effort
Sleep stage classification with ECG and respiratory effort
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DOI：
10.1088/0967-3334/36/10/2027
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发表时间：
2015-10-01
影响因子：
3.2
通讯作者：
Rolink, Jerome
中科院分区：
文献类型：
--
作者：
Fonseca, Pedro;Long, Xi;Rolink, Jerome
基于心肺信号的睡眠阶段自动分类已经引起越来越多的关注。与传统的基于多导睡眠图的手动评分相比，这些信号可以使用目前可用的先进的非侵入性技术来测量，有望应用于个人和连续的家庭睡眠监测。本文描述了一种方法，用于分类唤醒，快速眼动（REM）睡眠，非REM（NREM）浅睡眠和深睡眠的基础上30秒的时期。共提取142个特征，从心电图和呼吸电感体积描记法测量胸部呼吸努力。为了提高这些特征的质量，使用受试者特异性Z评分标准化和样条平滑来减少受试者间和受试者内变异性。一个修改后的顺序向前选择功能选择器程序，产生80个功能，同时防止引入偏差的交叉验证性能的估计。48名健康成人的PSG数据被用来验证我们的方法。使用线性判别分类器和10倍交叉验证，我们实现了科恩的kappa系数为0.49和69%的准确性在清醒，REM，轻，深睡眠的分类。这些值增加到kappa = 0.56和准确度= 80%时，分类问题减少到三个类，清醒，REM睡眠，NREM睡眠。
Automatic sleep stage classification with cardiorespiratory signals has attracted increasing attention. In contrast to the traditional manual scoring based on polysomnography, these signals can be measured using advanced unobtrusive techniques that are currently available, promising the application for personal and continuous home sleep monitoring. This paper describes a methodology for classifying wake, rapid-eye-movement (REM) sleep, and non-REM (NREM) light and deep sleep on a 30 s epoch basis. A total of 142 features were extracted from electrocardiogram and thoracic respiratory effort measured with respiratory inductance plethysmography. To improve the quality of these features, subject-specific Z-score normalization and spline smoothing were used to reduce between-subject and within-subject variability. A modified sequential forward selection feature selector procedure was applied, yielding 80 features while preventing the introduction of bias in the estimation of cross-validation performance. PSG data from 48 healthy adults were used to validate our methods. Using a linear discriminant classifier and a ten-fold cross-validation, we achieved a Cohen's kappa coefficient of 0.49 and an accuracy of 69% in the classification of wake, REM, light, and deep sleep. These values increased to kappa = 0.56 and accuracy = 80% when the classification problem was reduced to three classes, wake, REM sleep, and NREM sleep.