Enhancing EEG-based sleep staging efficiency with minimal channels through adversarial domain adaptation and active deep learning.

IF 3.8
Roya Ghasemigarjan, Mohammad Mikaeili, Seyed Kamaledin Setarehdan, Arash Saboori
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Abstract

Objective. Accurate sleep-stage classification is crucial for advancing both sleep research and healthcare applications. Traditional deep learning (DL) and domain adaptation (DA) methods often struggle due to the limited availability of labeled data in the target domain and their inability to capture the subtle distinctions between sleep-stage classes, which hampers classification accuracy.Approach. To address these limitations, we introduce a novel framework, adversarial domain adaptation with active deep learning (ADAADL). This framework combines adversarial learning with active learning (AL) strategies to improve feature alignment and effectively leverage unlabeled data. ADAADL employs two separate sleep-stage classifiers as discriminators, allowing for a more refined consideration of class boundaries during the feature alignment process. Moreover, it incorporates entropy measures alongside cross-entropy loss during training to make better use of the information from unlabeled data. The AL component (ADL) further enhances performance by iteratively selecting and labeling the most informative data points, thereby reducing annotation efforts and improving generalization to unseen data.Main results.Experimental evaluations on three benchmark EEG datasets demonstrate that ADAADL produces robust, transferable features, significantly outperforming existing DA methods in classification accuracy. This research advances sleep-stage classification techniques, offering improved accuracy for real-world applications and contributing to a deeper understanding of sleep dynamics.Significance. The proposed ADAADL framework advances the state of the art in sleep-stage classification by effectively leveraging unlabeled data and reducing labeling costs. It offers a scalable and accurate solution for real-world sleep monitoring applications and contributes to a deeper understanding of sleep dynamics through improved modeling of sleep stages.

通过对抗域适应和主动深度学习,以最小的通道增强基于脑电图的睡眠分期效率。
准确的睡眠阶段分类对于推进睡眠研究和医疗保健应用至关重要。传统的深度学习(DL)和领域适应(DA)方法经常因为目标领域中标记数据的可用性有限以及它们无法捕捉睡眠阶段类别之间的细微区别而陷入困境,这阻碍了分类的准确性。为了解决这些限制,我们引入了一个新的框架,主动深度学习的对抗域适应(ADAADL)。该框架将对抗性学习与主动学习策略相结合,以改善特征对齐并有效利用未标记数据。ADAADL使用两个独立的睡眠阶段分类器作为鉴别器,允许在特征对齐过程中更精细地考虑类边界。此外,它在训练过程中结合了熵度量和交叉熵损失,以更好地利用来自未标记数据的信息。主动学习组件(ADL)通过迭代地选择和标记最具信息量的数据点来进一步提高性能,从而减少注释工作并提高对未见数据的泛化。在三个基准脑电数据集上的实验评估表明,ADAADL产生了鲁棒性、可转移性强的特征,在分类精度上显著优于现有的数据处理方法。这项研究推进了睡眠阶段分类技术,为现实世界的应用提供了更高的准确性,并有助于更深入地了解睡眠动力学。
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