Optimal Shrinkage-aided Airflow Decomposition Algorithm (OSADA) and Cardiac Oscillation Recovery

ObjectiveCardiogenic oscillations (CO) in airflow signals contain valuable physiological information. However, accurately isolating CO from airflow signals, particularly in individuals with sleep apnea, remains a challenging signal processing problem. MethodWe introduce the Optimal Shrinkage-aided Airflow Decomposition Algorithm (OSADA), a novel approach for extracting CO from airflow signals while simultaneously recovering a CO-free, noise-free airflow signal, referred to as diaphragm-driven airflow (DDairflow). The algorithms performance is quantitatively evaluated using both a semi-real simulated database and real-world data with benchmark comparisons to existing methods, including the bandpass filter (BPF) and Savitzky-Golay smoothing filters (SGF). ResultFor the semi-real database, OSADA significantly outperforms BPF and SGF across multiple performance indices, including the normalized root mean square error (NRMSE) for CO and DDairflow recovery, as well as spectral energy indices of CO. For real-world data, OSADA also achieves superior performance in the data-driven spectral energy index of CO. ConclusionOSADA is the first algorithm specifically designed for CO recovery from single-channel airflow signals, without relying on additional channels, and is supported by theoretical foundations. Quantitative results suggest robust performance for both CO extraction and DDairflow recovery.