Sleep stage classification, including wakefulness (W), rapid eye movement (REM), and non- rapid eye movement (NREM) which includes three sleep stages that describe the depth of sleep, is one of the most critical steps in effective diagnosis and treatment of sleep-related disorders. Clinically, sleep staging is performed by domain experts through visual inspection of polysomnography (PSG) recordings, which is time-consuming, labor-intensive and often subjective in nature. Therefore, this study develops an automatic sleep staging system, which uses single channel electroencephalogram (EEG) signal, for convenience of wearing and less interference in the sleep, to do automatic identification of various sleep stages. To achieve the automatic sleep staging system, this study proposes a two-layer stacked ensemble model, which combines the advantages of random forest (RF) and LightGBM (LGB), where RF focuses on reducing the variance of the proposed model while LGB focuses on reducing the bias of the proposed model. Particularly, the proposed model introduces a class balance strategy to improve the N1 stage recognition rate. In order to evaluate the performance of the proposed model, experiments are performed on two datasets, including Sleep-EDF database (SEDFDB) and Sleep-EDF Expanded database (SEDFEDB). In the SEDFDB, the overall accuracy (ACC), weight F1-score (WF1), Cohen’s Kappa coefficient (Kappa), sensitivity of N1 (SEN-N1) obtained by proposed model are 91.2%, 0.916, 0.864 and 72.52% respectively using subject-non-independent test (SNT). In parallel, the ACC, WF1, Kappa, SEN-N1 obtained by proposed model are 82.4%, 0.751, 0.719 and 27.15% respectively using subject-independent test (SIT). Experimental results show that the performance of the proposed model are competitive with the state-of-the-art methods and results, and the recognition rate of N1 stage is significantly improved. Moreover, in the SEDFEDB, the experimental results indicate the robustness and generality of the proposed model.

中文翻译：

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基于两层堆叠集成模型的单通道脑电信号自动睡眠阶段分类

睡眠阶段分类，包括觉醒（W）、快速眼动（REM）和非快速眼动（NREM），包括描述睡眠深度的三个睡眠阶段，是有效诊断和治疗的最关键步骤之一睡眠相关疾病。在临床上，睡眠分期由领域专家通过对多导睡眠图 (PSG) 记录的目视检查来进行，这既费时又费力，而且通常具有主观性。因此，本研究开发了一种自动睡眠分期系统，利用单通道脑电图（EEG）信号，为佩戴方便，对睡眠干扰少，对各个睡眠阶段进行自动识别。为了实现自动睡眠分期系统，本研究提出了一个两层堆叠的集成模型，它结合了随机森林（RF）和 LightGBM（LGB）的优点，其中 RF 侧重于减少所提出模型的方差，而 LGB 侧重于减少所提出模型的偏差。特别是，所提出的模型引入了类平衡策略来提高 N1 阶段的识别率。为了评估所提出模型的性能，在两个数据集上进行了实验，包括睡眠-EDF 数据库（SEDFDB）和睡眠-EDF 扩展数据库（SEDFEDB）。在SEDFDB中，提出的模型获得的总体准确率（ACC）、权重F1-score（WF1）、Cohen's Kappa系数（Kappa）、N1灵敏度（SEN-N1）分别为91.2%、0.916、0.864和72.52%主题非独立测试（SNT）。同时，提出的模型获得的ACC、WF1、Kappa、SEN-N1分别为82.4%、0.751、0.719和27。15% 分别使用主题独立测试 (SIT)。实验结果表明，所提出模型的性能与最先进的方法和结果具有竞争力，N1阶段的识别率显着提高。此外，在 SEDFEDB 中，实验结果表明所提出模型的鲁棒性和通用性。