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

Abstract

Overnight pulse oximetry allows the relatively noninvasive estimation of peripheral blood haemoglobin oxygen saturations (SpO 2 ), and forms part of the typical polysomnogram (PSG) for investigation of obstructive sleep apnoea (OSA). While the raw SpO 2 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.

Digital oximetry biomarkers for assessing respiratory function: standards of measurement, physiological interpretation, and clinical use

“…Research on the use of existing and development of new oximetry biomarkers has mainly focused on the diagnosis of OSA, as echoed by five recent reviews in this field [7][8][9][10][11] . Although this paper will naturally somewhat overlap with these reviews, we present a new comprehensive review focusing on the physiological interpretation and clinical use of oximetry biomarkers, in the spirit of the work of Malik et al 3 in the field of HRV analysis.…”

“…Various categorizations of OBMs have been previously suggested 10,22 . We introduce a five-category classification scheme of our own, which we believe best reflects the literature and usage of these biomarkers in medical practice.…”

“…Regularity quantifies how often similar patterns are observed in the oximetry signal 10 . In the context of physiological time series analysis, approximate entropy (ApEn) 28 and sample entropy (SampEn) 29 have commonly been used as measures of the unpredictability (opposite of regularity).…”

“…These are, respectively, denoted DD100 μ and DD100 σ for the mean and the standard deviation computed across all desaturation depths. The idea of quantifying the desaturation depth has also been used by Terrill 10 . The desaturation depth may be an important factor for determination of the severity of OSA because it will reflect, for a given desaturation, the level of the hypoxic stress imposed.…”

The future of sleep‐disordered breathing: A public health crisis

“…7 Detailed oximetry analysis through time, frequency and non-linear analysis better characterizes the severity of desaturation events, which better reflect the burden of OSA and thereby may serve as improved predictors of OSA-related outcomes. 15 The sleep field finds itself at a very exciting time in health care. As the community, industry and governments expand their awareness of sleep as pillar of health and well-being, there is growing appetite for its measurement and management.…”