A review of approaches for analysing obstructive sleep apnoea-related patterns in pulse oximetry data

Abstract

Overnight pulse oximetry allows the relatively non-invasive estimation of peripheral blood haemoglobin oxygen saturations (SpO₂ ), and forms part of the typical polysomnogram (PSG) for investigation of obstructive sleep apnoea (OSA). While the raw SpO₂ signal can provide detailed information about OSA-related pathophysiology, this information is typically summarized with simple statistics such as the oxygen desaturation index (ODI, number of desaturations per hour). As such, this study reviews the technical methods for quantifying OSA-related patterns in oximetry data. The technical methods described in literature can be broadly grouped into four categories: (i) Describing the detailed characteristics of desaturations events; (ii) Time series statistics; (iii) Analysis of power spectral distribution (i.e. frequency domain analysis); and (d) Non-linear analysis. These are described and illustrated with examples of oximetry traces. The utilization of these techniques is then described in two applications. First, the application of detailed oximetry analysis allows the accurate automated classification of PSG-defined OSA. Second, quantifications which better characterize the severity of desaturation events are better predictors of OSA-related epidemiological outcomes than standard clinical metrics. Finally, methodological considerations and further applications and opportunities are considered.

Keywords: biomedical engineering; machine learning; oximetry; sleep apnoea, obstructive; sleep-disordered breathing.