Obstructive Sleep Apnea Detection via a Physiology-Informed Superlet-Ridge Framework for Cardiopulmonary Coupling

Accurate and practical screening of obstructive sleep apnea (OSA) from single-lead electrocardiogram (ECG) remains challenging, partly because conventional cardiopulmonary coupling (CPC) analysis does not fully capture the band-specific dynamics associated with apnea-related cardiorespiratory interactions. We propose a Physiology-Informed Superlet-Ridge Framework (PISRF), in which CPC is represented using a Physiology-Informed Superlet Transform (PISLT) with a multiplicative mode in the low-frequency (LF) band and an additive mode in the high-frequency (HF) band. This band-specific design is intended to better preserve pathological LF modulation and physiological HF coupling. Building on the resulting CPC representation, ridge-based decomposition is further used to extract the normalized HF ridge energy (HFRn) as a discriminative feature. On the PhysioNet Apnea-ECG dataset, PISLT-based CPC outperformed CPC derived from the continuous wavelet transform and wavelet synchrosqueezing transform, achieving 84.40% average accuracy and 80.17% average F1-score across classifiers. By further incorporating the HFRn ridge feature, PISRF improved performance to 85.01% average accuracy and 81.33% average F1-score. These results support PISRF as an interpretable and practically deployable ECG-based framework for OSA screening.