A novel algorithm for automatic diagnosis of sleep apnea from airflow and oximetry signals

Objective. Sleep apnea significantly decreases the quality of life. The apnea hypopnea index (AHI) is the main indicator for sleep apnea diagnosis. This study explored a novel automatic algorithm to diagnose sleep apnea from nasal airflow (AF) and pulse oximetry (SpO(2)) signals. Approach. Of the 988 polysomnography (PSG) records from the sleep heart health study (SHHS), 45 were randomly selected for the development of an algorithm and the remainder for validation (n = 943). The algorithm detects apnea events by a digitization process, following the determination of the peak excursion (peak-to-trough amplitude) from AF envelope. Hypopnea events were determined from the AF envelope and oxygen desaturation with correction to time lag in SpO(2). Total sleep time (TST) was estimated from an optimized percentage of artefact-free total recording time. AHI was estimated from the number of detected events divided by the estimated TST. The estimated AHI was compared to the scored SHHS data for performance evaluation. Main results. The validation showed good agreement between the estimated and scored AHI (intraclass correlation coefficient of 0.95 and mean 95% limits of agreement of -1.6 12.5 events h(-1)). The diagnostic accuracies were found: 90.7%, 91%, and 96.7% for AHI cut-off >= 5, >= 15, and >= 30 respectively. Significance. The new algorithm is accurate over other existing methods for the automatic diagnosis of sleep apnea. It is applicable to any portable sleep screeners especially for the home diagnosis of sleep apnea.