Title: MMUDA: Towards Robust Sleep Staging with Multi-Source Multi-Channel Unsupervised Domain Adaptation
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: Sleep Stage, Domain Adaptation, Domain-invariant feature learning

Abstract: The generalization of deep learning models for sleep staging across different datasets is severely hindered by domain shift, a critical obstacle for their clinical adoption. We introduce MMUDA, a novel framework that tackles the complex, real-world challenge of Multi-source Multi-channel Unsupervised Domain Adaptation. Our approach learns domain-invariant features from multiple labeled source domains and an unlabeled target domain through a carefully designed architecture. We employ dedicated encoders with channel attention to capture rich temporal context and enhance inter-channel feature fusion. To bridge the domain gap, MMUDA uniquely combines two complementary alignment strategies: Maximum Mean Discrepancy (MMD) explicitly minimizes the distribution discrepancy between domain pairs, while cross-domain contrastive learning (CL) ensures that the aligned features remain class-discriminative. This dual-alignment process is stabilized by a variational autoencoder (VAE) that encourages a more compact latent feature space. Comprehensive evaluations on several public sleep datasets show that MMUDA sets a new state-of-the-art in cross-domain sleep staging without requiring any target domain labels. Furthermore, we confirm its robustness and practical utility in locally collected hospital data. Our code will be released upon acceptance.