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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.