Objective evaluation of sleep is crucial to recognize sleep disorders and take effective interventions accordingly. Due to the shortcomings of the conventional polysomnographic acquisition, an automatic sleep scoring method based on a single-channel electroencephalogram (EEG) is vital in sleep medicine practice. In this study, we proposed a data-driven and robust automatic sleep staging scheme based on a single-channel EEG. With the decomposition of the EEG epochs using Ensemble Empirical Mode Decomposition (EEMD), we extracted various features using statistical, time-domain and nonlinear dynamics characteristics from not only the original EEG signal but also the decomposed intrinsic mode functions (IMFs). A classification model with the eXtreme Gradient Boosting (XGBoost) algorithm was trained and tested using 5-fold cross-validation on three different databases, i.e., the Sleep-EDF database available at Physionet, the DREAMS Subjects database and the database of Sleep Heart Health Study (SHHS). The results demonstrated that the analysis of IMFs derived from EEMD could provide substantial supplement to the classification of EEG sleep stages. In the tasks of 4-class and 5-class sleep staging, the proposed method achieved an accuracy of 93.1% and 91.9% for the Sleep-EDF database, 86.4% and 83.4% for the DREAMS Subjects database, and 87.5% and 85.8% for the SHHS database, respectively. Furthermore, our observation that prefrontal EEG derivations should be optimal choices might evoke promising application of wearable EEG devices on sleep monitoring because EEG signals can be easily obtained using dry electrodes on the forehead. Additionally, the proposed method is computationally efficient and should be valuable in real-time sleep-scoring.

睡眠的客观评估对于识别睡眠障碍并采取相应的有效干预措施至关重要。由于常规多导睡眠图采集的缺点，基于单通道脑电图（EEG）的自动睡眠评分方法在睡眠医学实践中至关重要。在这项研究中，我们提出了一种基于单通道脑电图的数据驱动且健壮的自动睡眠分期方案。通过使用集成经验模式分解（EEMD）对EEG历元进行分解，我们不仅使用原始的EEG信号，而且还通过分解的固有模式函数（IMF）使用统计，时域和非线性动力学特性提取了各种特征。使用eXtreme Gradient Boosting（XGBoost）算法的分类模型在三个不同的数据库（即Physionet上的Sleep-EDF数据库，DREAMS Subjects数据库和Sleep Heart Health数据库）上使用5倍交叉验证进行了训练和测试。研究（SHHS）。结果表明，对源自EEMD的IMF的分析可以为EEG睡眠阶段的分类提供实质性的补充。在4类和5类睡眠分期的任务中，该方法对Sleep-EDF数据库的准确性达到93.1％和91.9％，对DREAMS Subjects数据库的准确性达到86.4％和83.4％，以及87.5％和85.8％分别用于SHHS数据库。此外，我们的观察认为，额叶前脑电导数应为最佳选择，这可能会激发可穿戴式脑电图设备在睡眠监测中的应用前景，因为可以使用额头上的干电极轻松获得脑电图信号。另外，所提出的方法在计算上是有效的，并且在实时睡眠计分中应该是有价值的。