论文标题
用于标签有效睡眠阶段分类的自学学习：全面评估
Self-supervised Learning for Label-Efficient Sleep Stage Classification: A Comprehensive Evaluation
论文作者
论文摘要
在过去的几年中，基于脑电图的睡眠阶段分类（SSC）的深度学习取得了显着的进步。但是，这些模型的成功归因于拥有大量标记的数据进行培训，从而限制了它们在实际情况下的适用性。在这种情况下，睡眠实验室可以生成大量数据，但是标记这些数据可能很昂贵且耗时。最近，自我监督的学习（SSL）范式已成为克服标记数据稀缺的最成功的技术之一。在本文中，我们评估了SSL在几个标签制度中提高现有SSC模型的性能的功效。我们对三个SSC数据集进行了彻底的研究，我们发现只有5％的标记数据对经过预定的SSC模型进行微调可以通过完整标签来实现监督培训的竞争性能。此外，自我监管的预处理有助于SSC模型对数据不平衡和域转移问题更加强大。该代码可在https://github.com/emadeldeen24/eval_ssl_ssc上公开获取。
The past few years have witnessed a remarkable advance in deep learning for EEG-based sleep stage classification (SSC). However, the success of these models is attributed to possessing a massive amount of labeled data for training, limiting their applicability in real-world scenarios. In such scenarios, sleep labs can generate a massive amount of data, but labeling these data can be expensive and time-consuming. Recently, the self-supervised learning (SSL) paradigm has shined as one of the most successful techniques to overcome the scarcity of labeled data. In this paper, we evaluate the efficacy of SSL to boost the performance of existing SSC models in the few-labels regime. We conduct a thorough study on three SSC datasets, and we find that fine-tuning the pretrained SSC models with only 5% of labeled data can achieve competitive performance to the supervised training with full labels. Moreover, self-supervised pretraining helps SSC models to be more robust to data imbalance and domain shift problems. The code is publicly available at https://github.com/emadeldeen24/eval_ssl_ssc.
