Department of Electrical Engineering, National Ilan University, Yilan 26047, Taiwan.
Graduate Institute of Electronics Engineering, College of Electrical Engineering and Computer Science, National Taiwan University, Taipei 10617, Taiwan.
Sensors (Basel). 2021 Sep 9;21(18):6049. doi: 10.3390/s21186049.
Poor-quality sleep substantially diminishes the overall quality of life. It has been shown that sleep arousal serves as a good indicator for scoring sleep quality. However, patients are conventionally asked to perform overnight polysomnography tests to collect their physiological data, which are used for the manual judging of sleep arousals. Even worse, not only is this process time-consuming and cumbersome, the judgment of sleep-arousal events is subjective and differs widely from expert to expert. Therefore, this work focuses on designing an automatic sleep-arousal detector that necessitates only a single-lead electroencephalogram signal. Based on the stacking ensemble learning framework, the automatic sleep-arousal detector adopts a meta-classifier that stacks four sub-models: one-dimensional convolutional neural networks, recurrent neural networks, merged convolutional and recurrent networks, and random forest classifiers. This meta-classifier exploits both advantages from deep learning networks and conventional machine learning algorithms to enhance its performance. The embedded information for discriminating the sleep-arousals is extracted from waveform sequences, spectrum characteristics, and expert-defined statistics in single-lead EEG signals. Its effectiveness is evaluated using an open-accessed database, which comprises polysomnograms of 994 individuals, provided by PhysioNet. The improvement of the stacking ensemble learning over a single sub-model was up to 9.29%, 7.79%, 11.03%, 8.61% and 9.04%, respectively, in terms of specificity, sensitivity, precision, accuracy, and area under the receiver operating characteristic curve.
睡眠质量差会大大降低整体生活质量。研究表明，睡眠觉醒可以作为评估睡眠质量的一个很好的指标。然而，传统上要求患者进行整夜多导睡眠图测试以收集其生理数据，这些数据用于手动判断睡眠觉醒。更糟糕的是，这个过程不仅耗时且繁琐，而且睡眠觉醒事件的判断是主观的，不同专家之间存在很大差异。因此，这项工作专注于设计一个仅需要单导联脑电图信号的自动睡眠觉醒检测器。基于堆叠集成学习框架，自动睡眠觉醒检测器采用元分类器，该元分类器堆叠了四个子模型：一维卷积神经网络、循环神经网络、合并卷积和循环网络以及随机森林分类器。该元分类器利用深度学习网络和传统机器学习算法的优势来提高性能。从单导联 EEG 信号的波形序列、频谱特征和专家定义的统计信息中提取用于区分睡眠觉醒的嵌入式信息。其有效性使用 PhysioNet 提供的 994 个人的多导睡眠图组成的公开访问数据库进行评估。与单个子模型相比，堆叠集成学习在特异性、敏感性、精度、准确性和接收器工作特征曲线下面积方面的改进分别高达 9.29%、7.79%、11.03%、8.61%和 9.04%。
