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In Physiological measurement ; h5-index 36.0

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.

Sharma Manish, Kumar Kamlesh, Kumar Prince, Tan Ru-San, Acharya U Rajendra


Apnea detection using SpO2, Automated sleep apnea, Home-based detection of Apnea, Oximetry and Sleep Apnea, Review on Apnea