Authors : Odegwo, James Ifeanyi; Okpor, Margaret Dumebi; Ikedilo, Obiora Emeka; Okpomo, Eterigho Okpu; Obruche Chris; Osakwe Godwin Ohumaehuni; Victoria Onyeoma Odegwo

Volume/Issue : Volume 11 - 2026, Issue 8 - August

Google Scholar : https://tinyurl.com/fx3phkd3

DOI : https://doi.org/10.38124/ijisrt/26aug1586

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Sleep apnoea (SA) is a prevalent but underdiagnosed sleep-related breathing disorder associated with significant cognitive, cardiovascular, and metabolic consequences. Polysomnography (PSG), the clinical gold standard for diagnosis, is expensive, resource-intensive, and unsuitable for large-scale or continuous home-based monitoring. This paper presents a real-time sleep apnoea detection framework based solely on oxygen saturation (SpO₂) signals, enabling a simplified and noninvasive diagnostic approach. Experiments were conducted using recordings from the publicly available CAP Sleep Database hosted on PhysioNet, which contains annotated overnight polysomnography data acquired in controlled clinical environments. SpO₂ signals were segmented into standard 30-second windows, and time-domain statistical features were extracted to characterise oxygen desaturation patterns. A Random Forest classifier was employed to discriminate apnoearelated desaturation events from normal breathing episodes.

Keywords : Sleep Apnoea, Oxygen Saturation, SpO₂, Home-Based Monitoring, Machine Learning, Polysomnography.

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