Pulse oximetry SpO ₂ signal for automated identification of sleep apnea: a review and future trends

Abstract

Sleep apnea (SA) is characterized by intermittent episodes of apnea or hypopnea—paused or reduced breathing, respectively—each lasting at least ten seconds that occur during sleep. SA has an estimated global prevalence of 200 million and is associated with medical co-morbidities, and sufferers are also more likely to sustain traffic- and work-related injury due to daytime somnolence. SA is amenable to treatment if detected early. Polysomnography (PSG) involving multi-channel signal acquisition is the reference standard for diagnosing SA but is onerous and costly. For home-based detection of SA, single-channel SpO2 signal acquisition using portable pulse oximeters is feasible. Machine (ML) and deep learning (DL) models have been developed for the automated classification of SA versus no SA using SpO2 signals alone. In this work, we review studies published between 2012 and 2022 on the use of ML and DL for SpO2 signal-based diagnosis of SA. . A literature search based on PRISMA recommendations yielded 297 publications, of which 31 were selected after considering the inclusion and exclusion criteria. There were 20 ML and 11 DL models; their methods, differences, results, merits, and limitations were discussed. Many studies reported encouraging performance, which indicates the utility of SpO2 signals in wearable devices for home-based SA detection.

Data Interoperability for Ambulatory Monitoring of Cardiovascular Disease: A Scientific Statement From the American Heart Association

“…62 Several devices are being tested to measure sleep-related physiology in the ambulatory setting to simplify diagnosis and to augment or potentially replace polysomnography in selected cases. 63 Emerging devices use electrocardiographic and phonocardiographic sensors, oxygen sensors that can detect desaturations, 64 photoplethysmography to detect abnormalities in pulse waveforms, and sound sensors to detect snoring, as well as electroencephalographic and electromyographic sensors. 62…”

Adoption of Transformer Neural Network to Improve the Diagnostic Performance of Oximetry for Obstructive Sleep Apnea

“…Recent research has shown that DL can be used to detect sleep apnea using SpO2 only with an acceptable degree of accuracy [ 20 ]. For example, one study found that a DL model based on an ANN architecture was able to detect sleep apnea with an accuracy of 97.8% [ 21 ].…”

“…For example, one study found that a DL model based on an ANN architecture was able to detect sleep apnea with an accuracy of 97.8% [ 21 ]. Generally, four main types of DL networks were widely used to detect OSA from SpO2 [ 20 ]. Earlier, a deep vanilla neural network (DNN) was used to learn simple patterns [ 22 , 23 ].…”

Time frequency distribution and deep neural network for automated identification of insomnia using single channel EEG-signals

“…metabolic waste that accumulates during awakeness [1], [2]. In addition, reorganization and promotion of long-term memory is also done by good sleep.…”