Sleep staging with supervised learning requires a large amount of labeled data that are time-consuming and expensive to collect. Semi-supervised learning is widely used to improve classification performance by combining a small amount of labeled data with a large amount of unlabeled data. The accuracy of pseudo-labels in semi-supervised learning may influence the performance of classifier. Based on semi-supervised sparse representation classification, this study proposed an improved sparse concentration index to estimate the confidence of pseudo-labels data for sleep EEG recognition considering both interclass differences and intraclass concentration. In view of class imbalance in sleep EEG data, the synthetic minority oversampling technique was also improved to remove mixed samples at the boundary between minority and majority classes. The results showed that the proposed method achieved better classification performance, in which the classification accuracy after class balancing was obviously higher than that before class balancing. The findings of this study will be beneficial for application in sleep monitoring devices and sleep-related diseases.

中文翻译：

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具有不平衡样本集的睡眠脑电图识别的半监督稀疏表示分类

使用监督学习进行睡眠分期需要大量标记数据，这些数据收集起来既费时又昂贵。半监督学习被广泛用于通过将少量标记数据与大量未标记数据相结合来提高分类性能。半监督学习中伪标签的准确性可能会影响分类器的性能。本研究基于半监督稀疏表示分类，提出了一种改进的稀疏浓度指数来估计睡眠脑电图识别的伪标签数据的置信度，同时考虑类间差异和类内浓度。鉴于睡眠脑电图数据的类别不平衡，还改进了合成少数过采样技术，以去除少数类和多数类之间边界的混合样本。结果表明，该方法取得了较好的分类性能，其中类平衡后的分类准确率明显高于类平衡前。这项研究的结果将有利于睡眠监测设备和睡眠相关疾病的应用。