Stage 1 (S1) and REM sleep are the two key stages in EEG-based sleep stage classification, which are of great significance to the study of neurocognitive ability and sleep diseases. Recently, various methods have been widely studied, and achieved good classification performance, however, most existing studies have a common problem of the low detection rate of S1 and REM sleep. In this paper, we focus on improving the detection performance of S1 and REM sleep and present an effective multi-model fusion method by using hybrid-channel EEG signals, which consists of two parts: the detection of merged stage of S1 and REM sleep and the classification between these two stages. First, we detect S1 and REM sleep by distinguishing the merged stage from other sleep stages using C-SVM model and single-channel EEG signals. To overcome the influence caused by class imbalance, a one-class OC-SVM model of the merged stage is established to correct S1 and REM sleep from the misclassified negative samples. Then, through analyzing the EEG characteristic between S1 and REM sleep and extracting the classification features of multiple sub-bands, we classify S1 and REM sleep using two-channel EEG signals. Finally, the proposed method is tested and analyzed for commonly used dataset of Sleep-EDFX. The results show that this method can effectively detect S1 and REM sleep and promote the application of sleep quality evaluation, fatigue detection, sleep disease diagnosis.

1期（S1）和REM睡眠是基于EEG的睡眠阶段分类中的两个关键阶段，对研究神经认知能力和睡眠疾病具有重要意义。近来，已经对各种方法进行了广泛的研究，并且获得了良好的分类性能，然而，大多数现有的研究具有S1和REM睡眠的低检测率的共同问题。在本文中，我们着重于提高S1和REM睡眠的检测性能，并提出了一种使用混合通道EEG信号的有效多模型融合方法，该方法包括两部分：S1和REM睡眠的融合阶段的检测以及这两个阶段之间的分类。首先，我们通过使用C-SVM模型和单通道EEG信号将合并阶段与其他睡眠阶段区分开来检测S1和REM睡眠。为了克服类不平衡引起的影响，建立了合并阶段的一类OC-SVM模型，以从分类错误的阴性样本中纠正S1和REM睡眠。然后，通过分析S1和REM睡眠之间的EEG特征并提取多个子带的分类特征，我们使用两通道EEG信号对S1和REM睡眠进行分类。最后，针对常用的Sleep-EDFX数据集对提出的方法进行了测试和分析。结果表明，该方法可以有效检测S1和REM睡眠，促进睡眠质量评估，疲劳检测，睡眠疾病诊断的应用。通过分析S1和REM睡眠之间的EEG特征并提取多个子带的分类特征，我们使用两通道EEG信号对S1和REM睡眠进行分类。最后，针对常用的Sleep-EDFX数据集对提出的方法进行了测试和分析。结果表明，该方法可以有效检测S1和REM睡眠，促进睡眠质量评估，疲劳检测，睡眠疾病诊断的应用。通过分析S1和REM睡眠之间的EEG特征并提取多个子带的分类特征，我们使用两通道EEG信号对S1和REM睡眠进行分类。最后，针对常用的Sleep-EDFX数据集对提出的方法进行了测试和分析。结果表明，该方法可以有效检测S1和REM睡眠，促进睡眠质量评估，疲劳检测，睡眠疾病诊断的应用。