Machine learning-based automatic sleep apnoea and severity level classification using ECG and SpO

Autor:	Gizeaddis Lamesgin, Simegn, Hundessa Daba, Nemomssa, Mikiyas Petros, Ayalew
Rok vydání:	2022
Předmět:	Electrocardiography Sleep Apnea Syndromes Support Vector Machine Polysomnography Humans Algorithms
Zdroj:	Journal of medical engineeringtechnology. 46(2)
ISSN:	1464-522X
Popis:	Sleep apnoea is a potentially serious sleep disorder that is characterised by repetitive episodes of breathing interruptions. Traditionally, sleep apnoea is commonly diagnosed in an attended sleep laboratory setting using polysomnography (PSG). The manual diagnosis of sleep apnoea using PSG is, however complex, and time-consuming, as many physiological variables are usually measured overnight using numerous sensors attached to patients. In PSG sleep laboratories, an expert human observer is required to work overnight, and the diagnosis accuracy is dependent on the physician's experience. A quantitative and objective method is required to improve the diagnosis efficacy, decrease the complexity and diagnosis time and to ensure a more accurate diagnosis. The purpose of this study was then to develop an automatic sleep apnoea and severity classification using a simultaneously recorded electrocardiograph (ECG) and saturation of oxygen (SpO
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=pmid________::e0de5651df8e59b791f3aae1767ae256 https://pubmed.ncbi.nlm.nih.gov/35060829 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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