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arXiv · 2607.25197

LGFNet: A CTC-Guided Local-Global Fusion Framework for Single-Channel Sleep Staging

Chongjian Wang·Zhenghang Hou·Junjie Gao·Xiaofang Zhong·Shiyuan Han·Tong Zhang
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Abstract

Sleep staging remains challenging due to long-range temporal dependencies, ambiguous stage transitions-particularly in N1-and substantial distribution shifts across subjects, sampling rates, and EEG montages. These difficulties are further amplified in single-channel, low-latency scenarios required by wearable and real-world applications. To address these issues, we propose LGFNet, a CTC-guided sequence-to-sequence framework for robust sleep staging. LGFNet introduces a Local-Global Fusion encoder that jointly models fine-grained temporal dynamics and long-range sleep structure, overcoming the limitations of conventional serial hybrid architectures. A CTC-Attention joint training paradigm is adopted to unify temporal alignment with context-dependent modeling, enabling more accurate recognition of stage boundaries and transitions. Furthermore, a three-stage decoding strategy is devised, leveraging CTC-guided decoding and Viterbi-based smoothing to reduce error accumulation and enforce physiological consistency. Extensive cross-dataset evaluations on five public benchmarks demonstrate that LGFNet consistently outperforms state-of-the-art single-channel methods. In particular, on Sleep-EDF-78, LGFNet surpasses DMIN by +1.27% accuracy, +1.74% macro-F1, and +1.93% kappa, with pronounced gains on N1 and transition segments, highlighting its robustness and strong generalization across diverse sampling rates, montages, and recording environments.

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Chongjian Wang, Zhenghang Hou, Junjie Gao, Xiaofang Zhong, Shiyuan Han, Tong Zhang. 2026-08-11. LGFNet: A CTC-Guided Local-Global Fusion Framework for Single-Channel Sleep Staging. https://arxiv.org/abs/2607.25197

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