Lyu Juntong, Chen Ziyang, Shi Wenbin, Yeh Chien-Hung
School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China.
School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China; Key Laboratory of Brain Health Intelligent Evaluation and Intervention (Beijing Institute of Technology), Ministry of Education, Beijing 100081, China.
Comput Methods Programs Biomed. 2025 Nov;271:108996. doi: 10.1016/j.cmpb.2025.108996. Epub 2025 Jul 26.
Sleep staging is pivotal in assessing sleep quality and diagnosing sleep-related disorders. Although previous efforts in sleep classification have achieved considerable success, individual differences arising from factors such as age, gender, and ethnicity continue to pose significant challenges to the generalization capability of deep neural networks, compromising their performance in subject-specific sleep staging tasks.
To address this challenge, we proposed a novel framework, DDAST, which leverages a discrepancy-based learning framework to effectively solve the domain shift problem inherent in the unlabeled target domain of sleep staging. First, we designed an adaptive domain-specific batch normalization to merge statistical information from the source domain (training data) into the target domain (testing data), especially for the small target data size condition. This reduces the uncertainty in estimating moments of the target domain, thereby improving the classification of target domain data. Second, we combined the self-training scheme with a discrepancy-based unsupervised learning strategy to develop a cross-subject sleep staging framework, which utilizes target domain pseudo-labels to align the fine-grained distributions of the source and target domains effectively.
The proposed framework was evaluated on two datasets through cross-validation experiments, achieving an accuracy of 89.7 % and 84.3 % on the MASS-SS3 and ISRUC-S3 datasets, respectively, outperforming other baseline methods. The effectiveness of different modules of the proposed framework was verified through ablation experiments. Visualization of feature representation also reveals a better alignment between the source and target domains after applying the proposed method, which suggests the proposed framework can effectively solve the domain shift problem in subject-specific sleep staging tasks.
This study presents a domain adaptation framework targeting subject-specific sleep classification, which holds promise in sleep-related disorders diagnosis as well as clinical sleep monitoring.
睡眠分期对于评估睡眠质量和诊断睡眠相关疾病至关重要。尽管先前在睡眠分类方面的努力取得了相当大的成功，但年龄、性别和种族等因素导致的个体差异仍然给深度神经网络的泛化能力带来重大挑战，影响其在特定个体睡眠分期任务中的表现。
为应对这一挑战，我们提出了一种新颖的框架DDAST，它利用基于差异的学习框架有效解决睡眠分期未标记目标域中固有的域转移问题。首先，我们设计了一种自适应的特定域批归一化，将源域（训练数据）的统计信息合并到目标域（测试数据）中，特别是针对目标数据量较小的情况。这减少了估计目标域矩时的不确定性，从而改善了目标域数据的分类。其次，我们将自训练方案与基于差异的无监督学习策略相结合，开发了一个跨个体睡眠分期框架，该框架利用目标域伪标签有效对齐源域和目标域的细粒度分布。
通过交叉验证实验在两个数据集上对提出的框架进行了评估，在MASS - SS3和ISRUC - S3数据集上分别达到了89.7%和84.3%的准确率，优于其他基线方法。通过消融实验验证了所提出框架不同模块的有效性。特征表示的可视化还显示，应用所提出的方法后，源域和目标域之间的对齐更好，这表明所提出的框架可以有效解决特定个体睡眠分期任务中的域转移问题。
本研究提出了一个针对特定个体睡眠分类的域适应框架，在睡眠相关疾病诊断以及临床睡眠监测方面具有应用前景。
