Opportunities for utilizing polysomnography signals to characterize obstructive sleep apnea subtypes and severity

Abstract

Background: Obstructive sleep apnea (OSA) is a heterogeneous sleep disorder with many pathophysiological pathways to disease. Currently, the diagnosis and classification of OSA is based on the apnea-hypopnea index, which poorly correlates to underlying pathology and clinical consequences. A large number of in-laboratory sleep studies are performed around the world every year, already collecting an enormous amount of physiological data within an individual. Clinically, we have not yet fully taken advantage of this data, but combined with existing analytical approaches, we have the potential to transform the way OSA is managed within an individual patient. Currently, respiratory signals are used to count apneas and hypopneas, but patterns such as inspiratory flow signals can be used to predict optimal OSA treatment. Electrocardiographic data can reveal arrhythmias, but patterns such as heart rate variability can also be used to detect and classify OSA. Electroencephalography is used to score sleep stages and arousals, but specific patterns such as the odds-ratio product can be used to classify how OSA patients responds differently to arousals.

Objective: In this review, we examine these and many other existing computer-aided polysomnography signal processing algorithms and how they can reflect an individual's manifestation of OSA.

Significance: Together with current technological advance, it is only a matter of time before advanced automatic signal processing and analysis is widely applied to precision medicine of OSA in the clinical setting.

Figure 1:

Summary of polysomnography physiological signal features relevant to obstructive sleep apnea, potential analytical approaches and the goals of understanding these data towards personalized medicine for this disorder. Abbreviations: CAP: cyclic alternating pattern; ECG: electrocardiogram; EEG: electroencephalogram; SpO2: oxygen saturation; PPG: photoplethysmography. Some illustrations used with permission and adapted from (Petryszak et al 2016)

Figure 2

Examples of arousal with different intensity scales (0-least intense to 9-most intense) in the same patient. C3/A2 and C4/A1 are central electroencephalograms. Figure used with permission (Azarbarzin et al 2014)