Automatic Detection of Total Sleep Time using Nasal Airflow and Pulse Oximetry Signals

The severity of obstructive sleep apnea (OSA) is based on apnea hypopnea index, which is dependent on total sleep time (TST). TST is measured with the electrode-dependent, costly, and labor-intensive electroencephalography (EEE) recording derived from polysomnography. The TST estimation problem has been largely overcome by simplified screening alternatives, where total recording time (TRT) or a fixed percentage of TRT is used as TST. This study proposed an automatic algorithm to estimate TST from airflow (AF) and oximetry (SpO2) by applying a subject-adaptive sleep-wake classification strategy. The algorithm took AF and SpO2 signals as input, pre-processed AF (smoothing, filtering, peak-excursion) and SpO2 (round-off and correction). After segmenting both signals into 30 second epochs, different features were extracted. A data-driven adaptive threshold was applied to distinguish epochs of sleep from wake, from which TST was estimated. On a held-out testing set (N = 68 subjects), the estimated parameters were compared against annotation. The method resulted in negligible systematic underestimation at the population level (Pearson’s correlation coefficient, r = 0.463). A minimal underestimation, with a mean bias of -0.004 hours and a limits of agreement of −2.147 to +2.139 hours was resulted in between the estimated and annotated TST. The performance of the designed algorithm can’t be compared with EEG-based studies, and there is no such study of estimating TST from AF and SpO2. Hence, the method can be applied to OSA screening device, since the apnea and hypopnea events screening is based on these two signals.