Leveraging Synchrosqueezing Transform (SST)-based representations in a dual-stream attention framework to enhance sleep apnea detection and subtyping.

Introduction: Sleep apnea and hypopnea syndrome (SAHS) is a prevalent disorder with profound adverse effects on health and overall quality of life, thereby necessitating the development of accurate and accessible screening tools. Electrocardiogram (ECG)-based analysis, being non-invasive and readily deployable in low-cost hardware, offers a particularly convenient approach for SAHS screening and preliminary diagnosis. However, conventional time-frequency analysis often fails to capture the subtle yet critical patterns in ECG signals due to the Heisenberg uncertainty principle, leading to limited resolution and information loss. Methods: To overcome these limitations, this study proposes a Dual Stream Cross Attention Fusion Network (DSCAFNet) based on the uncertainty-mitigated time-frequency representations generated via Synchrosqueezing Transform (SST). The framework uniquely constructs two complementary, high-fidelity SST-based representations, which are strategically designed to provide distinct yet synergistic perspectives on the complex, non-stationary dynamics of SAHS. A dedicated cross-attention fusion module then harnesses these complementary views, enabling the model to discriminatively integrate multi-resolution features for significantly enhanced pattern recognition. Results: Extensively evaluated on the public Apnea-ECG dataset, DSCAFNet achieves an accuracy of 0.9572, a sensitivity of 0.9575, a specificity of 0.9584, and an F1-score of 0.9557, performing on par with state-of-the-art methods. More importantly, rigorous validation on a private Huashan-apnea dataset yields an accuracy of 0.9003 for binary classification and 0.7564 for four-class subtyping, demonstrating strong effectiveness and generalization. Conclusion: These consistent results across datasets highlight DSCAFNet as a promising framework for intelligent and accessible SAHS screening, with potential for integration into portable data acquisition systems combined with cloud-based analysis.