LAUSR

OBJECTIVE Obstructive sleep apnea is characterized by a number of airway obstructions. Esophageal pressure manometry (EPM) based estimation of consecutive peak to trough differences (ΔPes) is the gold standard method to quantify the severity of airway obstructions. However, the procedure is rarely available in sleep laboratories due to invasive nature. There is a clinical need for a simplified, scalable technology that can quantify the severity of airway obstructions. In this paper, we address this and propose a pioneering technology, centered on sleep related respiratory sound (SRS) to predict overnight ΔPes signal. APPROACH We recorded streams of SRS using a bedside iPhone 7 smartphone from subjects undergoing diagnostic polysomnography (PSG) studies and EPM was performed concurrently. Overnight data was divided into epochs of 10 s duration with 50% overlap. Altogether, we extracted 42 181 such epochs from 13 subjects. Acoustic features and features from the two PSG signals serve as an input to train a machine learning algorithm to achieve mapping between non-invasive features and ΔPes values. A testing dataset of 14 171 epochs from four new subjects was used for validation. MAIN RESULTS The SRS based model predicted the ΔPes with a median of absolute error of 6.75 cmH2O (±0.59, r = 0.83(±0.03)). When information from the PSG were combined with the SRS, the model performance became: 6.37cmH2O (±1.02, r = 0.85(±0.04)). SIGNIFICANCE The smart phone based SRS alone, or in combination with routinely collected PSG signals can provide a non-invasive method to predict overnight ΔPes. The method has the potential to be automated and scaled to provide a low-cost alternative to EPM.