STADIUS Center for Dynamical Systems, Signal Porcessing and Data Analytics - Department of Electrical Engineering (ESAT), KU Leuven, Kasteelpark Arenberg 10, Leuven, 3001, Belgium.
School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, United Kingdom of Great Britain and Northern Ireland.
J Neural Eng. 2022 Jun 24;19(3). doi: 10.1088/1741-2552/ac6ca8.
The recent breakthrough of wearable sleep monitoring devices has resulted in large amounts of sleep data. However, as limited labels are available, interpreting these data requires automated sleep stage classification methods with a small need for labeled training data. Transfer learning and domain adaptation offer possible solutions by enabling models to learn on a source dataset and adapt to a target dataset.In this paper, we investigate adversarial domain adaptation applied to real use cases with wearable sleep datasets acquired from diseased patient populations. Different practical aspects of the adversarial domain adaptation framework are examined, including the added value of (pseudo-)labels from the target dataset and the influence of domain mismatch between the source and target data. The method is also implemented for personalization to specific patients.The results show that adversarial domain adaptation is effective in the application of sleep staging on wearable data. When compared to a model applied on a target dataset without any adaptation, the domain adaptation method in its simplest form achieves relative gains of 7%-27% in accuracy. The performance in the target domain is further boosted by adding pseudo-labels and real target domain labels when available, and by choosing an appropriate source dataset. Furthermore, unsupervised adversarial domain adaptation can also personalize a model, improving the performance by 1%-2% compared to a non-personalized model.In conclusion, adversarial domain adaptation provides a flexible framework for semi-supervised and unsupervised transfer learning. This is particularly useful in sleep staging and other wearable electroencephalography applications. (Clinical trial registration number: S64190.).
可穿戴睡眠监测设备的最新突破产生了大量的睡眠数据。然而，由于可用的标签有限，这些数据的解释需要自动化的睡眠阶段分类方法，并且需要很少的有标签的训练数据。迁移学习和领域自适应通过使模型能够在源数据集上学习并适应目标数据集，提供了可能的解决方案。在本文中，我们研究了对抗性领域自适应在实际案例中的应用，这些案例使用了从患病患者群体中采集的可穿戴睡眠数据集。检查了对抗性领域自适应框架的不同实际方面，包括来自目标数据集的（伪）标签的附加值以及源数据和目标数据之间的域不匹配的影响。该方法还针对特定患者进行了个性化实施。结果表明，对抗性领域自适应在可穿戴数据的睡眠分期应用中是有效的。与应用于没有任何自适应的目标数据集的模型相比，最简单形式的域自适应方法在准确性方面的相对增益为 7%-27%。当可用时，通过添加伪标签和真实目标域标签，并选择适当的源数据集，可以进一步提高目标域的性能。此外，无监督对抗性领域自适应还可以对模型进行个性化设置，与非个性化模型相比，性能提高 1%-2%。总之，对抗性领域自适应为半监督和无监督迁移学习提供了一个灵活的框架。这在睡眠分期和其他可穿戴脑电图应用中特别有用。（临床试验注册号：S64190）。
