A Joint Optimization Guided Deep Learning Model based on CNN and Channel-Wise Transformers for Robust Sleep Stage...

Sleep stage classification is a critical task in sleep research, with significant implications for diagnosing and treating sleep disorders. Traditional methods rely on manual scoring of polysomnography (PSG) data, which is time-consuming and prone to human error. While recent advances in deep learning have enabled automated sleep stage classification, challenges persist in handling the complex, non-linear patterns of physiological signals. Existing models are often computationally expensive, require sophisticated feature extraction methods, and are unsuitable for real-time implementation. To address these limitations, we propose a lightweight and efficient dual-branch deep-learning model that leverages the feature extraction capabilities of CNNs and the channel-wise attention mechanisms of Transformers. Unlike conventional transformers, it avoids excessive computational complexity while effectively capturing both local and global dependencies in physiological signals. The model is validated on four benchmark datasets-SleepEDF-20, SleepEDF-78, SleepEDFx, and SHHS-and demonstrates superior performance compared to several baseline algorithms. Our proposed algorithm achieves state-of-the-art results across all datasets, highlighting its robustness and scalability for real-world applications. The code for the proposed algorithm is publicly available at link, enabling reproducibility and further research. Combining the strengths of CNNs and Transformers, it offers a promising solution for accurate and efficient sleep stage classification, paving the way for improved diagnosis and treatment of sleep disorders. The code is available at https://github.com/ang-frozen/embc2025.