Rationale: Determining whether an individual has obstructive or central sleep apnea is fundamental to selecting the appropriate treatment. Objectives: Here we derive an automated breath-by-breath probability of obstruction, as a surrogate of gold-standard upper airway resistance, using hallmarks of upper airway obstruction visible on clinical sleep studies. Methods: From five nocturnal polysomnography signals (airflow, thoracic and abdominal effort, oxygen saturation, and snore), nine features were extracted and weighted to derive the breath-by-breath probability of obstruction (Pobs). A development and initial test set of 29 subjects (development = 6, test = 23) (New York, NY) and a second test set of 39 subjects (Solingen, Germany), both with esophageal manometry, were used to develop Pobs and validate it against gold-standard upper airway resistance. A separate dataset of 114 subjects with 2 consecutive nocturnal polysomnographies (New York, NY) without esophageal manometry was used to assess the night-to-night variability of Pobs. Measurements and Main Results: A total of 1,962,229 breaths were analyzed. On a breath-by-breath level, Pobs was strongly correlated with normalized upper airway resistance in both test sets (set 1: cubic adjusted [adj.] R2 = 0.87, P < 0.001, area under the receiver operating characteristic curve = 0.74; set 2: cubic adj. R2 = 0.83, P < 0.001, area under the receiver operating characteristic curve = 0.7). On a subject level, median Pobs was associated with the median normalized upper airway resistance (set 1: linear adj. R2 = 0.59, P < 0.001; set 2: linear adj. R2 = 0.45, P < 0.001). Median Pobs exhibited low night-to-night variability [intraclass correlation(2, 1) = 0.93]. Conclusions: Using nearly 2 million breaths from 182 subjects, we show that breath-by-breath probability of obstruction can reliably predict the overall burden of obstructed breaths in individual subjects and can aid in determining the type of sleep apnea.
Keywords: airflow limitation; esophageal pressure swings; machine learning; sleep apnea; upper airway resistance.