Pulse Rate Variability Analysis to Enhance Oximetry as at-Home Alternative for Sleep Apnea Diagnosing

This study focuses on the at-home Sleep apnea-hypopnea syndrome (SAHS) severity estimation. Three percent oxygen desaturation index \( (ODI_{3} ) \) from nocturnal pulse-oximetry has been commonly evaluated as simplified alternative to polysomnography (PSG), the standard in-hospital diagnostic test. However, \( ODI_{3} \) has shown limited ability to detect SAHS as it only sums up information from desaturation events. Other physiological signs of SAHS can be found in respiratory and cardiac signals, providing additional helpful data to establish SAHS and its severity. Pulse rate variability time series (PRV), also derived from nocturnal oximetry, is considered a surrogate for heart rate variability, which provides both cardiac and respiratory information. In this study, 200 oximetric recordings obtained at patients home were involved, divided into training (50%) and test (50%) groups. \( ODI_{3} \) and PRV were obtained from them, the latter being characterized by the extraction of statistical features in time domain, as well as the spectral entropy from the commonly used very low (0–0.04 Hz.), low (0.04–0.15 Hz.), and high (0.15–0.4 Hz.) frequency bands. The \( ODI_{3} \) and PRV features were joined in a multi-layer perceptron artificial neural network (MLP), trained to estimate the apnea-hypopnea index (AHI), which is the PSG-derived parameter used to diagnose SAHS. Our results showed that single \( ODI_{3} \) rightly assigned 62.0% of the subjects from the test group into one out the four SAHS severity degrees, reaching 0.470 Cohens kappa, and 0.840 intra-class correlation coefficient (ICC) with the actual AHI (accuracies of 90.0, 88.0 and 82.0% in the increasing AHI cutoffs used to define SAHS severity). By contrast, our MLP model rightly assigned 75.0% of the subjects into their corresponding SAHS severity level, reaching 0.614 \( \kappa \) and 0.904 ICC (accuracies of 93.0, 88.0 and 90.0%). These results suggest that SAHS diagnosis could be accurately conducted at-patients home by combining \( ODI_{3} \) and PRV from nocturnal oximetry