Automatic Sleep Staging from CPAP Airflow using a Dual Fusion Multi-Period Convolutional Neural Network

Background: Continuous Positive Airway Pressure (CPAP) therapy is the standard treatment for obstructive sleep apnea-hypopnea syndrome, yet its use as a passive sleep dynamics monitoring remains limited. CPAP devices record only airflow signals, called CPAP-flow, which constantly interact with the pressure delivered by the device. This interaction renders the signal vulnerable to device-related artifacts and inter-/intra-patient variability, posing significant challenges to its repurposing for monitoring sleep dynamics. Methods: Motivated by neural network-based studies of sleep-wake transition from CPAP-flow, we leverage existing physiological knowledge and introduce a Dual Fusion Multi-Period Convolutional Neural Network (DFMP-CNN) model. This deep learning architecture leverages multiple period-specific convolutional kernels and a dual-fusion mechanism to jointly encode known short- and long-range temporal dependencies in CPAP-flow, overcoming limitations of traditional fixed-scale models. Results: Extensive experiments demonstrate that DFMP-CNN achieves state-of-the-art performance in CPAP-based sleep staging. On the Yale dataset, it achieves 78.5\% accuracy (Cohen's kappa=0.605), with a best case of kappa=0.886; on the Duke dataset, it reaches 73.6% accuracy (kappa=0.524), with a best case of kappa=0.805. Cross-dataset evaluations confirm the model's transferability across clinical centers and device types, while feature and fusion ablation studies highlight its robustness. Conclusions: DFMP-CNN provides an unobtrusive approach for sleep monitoring using CPAP devices, providing a dual-purpose platform for therapy and longitudinal assessment. Significance: The robust performance of DFMP-CNN across datasets and device types underscores its potential to improve clinical assessment and optimize therapy management.