Objective. Automatic sleep staging models suffer from an inherent class imbalance problem (CIP), which hinders the classifiers from achieving a better performance. To address this issue, we systematically studied sleep electroencephalogram data augmentation (DA) approaches. Furthermore, we modified and transferred novel DA approaches from related research fields, yielding new efficient ways to enhance sleep datasets. Approach. This study covers five DA methods, including repeating minority classes, morphological change, signal segmentation and recombination, dataset-to-dataset transfer, as well as generative adversarial network (GAN). We evaluated these mentioned DA methods by a sleep staging model on two datasets, the Montreal archive of sleep studies (MASS) and Sleep-EDF. We used a classification model with a typical convolutional neural network architecture to evaluate the effectiveness of the mentioned DA approaches. We also conducted a comprehensive analysis ...

中文翻译：

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EEG 数据增强：针对睡眠分期任务中的类别不平衡问题

客观的。自动睡眠分级模型存在固有的类不平衡问题（CIP），这阻碍了分类器获得更好的性能。为了解决这个问题，我们系统地研究了睡眠脑电图数据增强 (DA) 方法。此外，我们从相关研究领域修改和转移了新的 DA 方法，产生了增强睡眠数据集的新有效方法。方法。这项研究涵盖了五种 DA 方法，包括重复少数类、形态变化、信号分割和重组、数据集到数据集的传输以及生成对抗网络 (GAN)。我们通过两个数据集（蒙特利尔睡眠研究档案 (MASS) 和 Sleep-EDF）上的睡眠分期模型评估了这些提到的 DA 方法。我们使用具有典型卷积神经网络架构的分类模型来评估上述 DA 方法的有效性。我们还进行了综合分析...