Automatic diagnosis of sleep apnea from biomedical signals using artificial intelligence techniques: Methods, challenges, and future works

Moridian, P; Shoeibi, A; Khodatars, M; Jafari, M; Pachori, RB; Khadem, A; Alizadehsani, R; Ling, SH

Automatic diagnosis of sleep apnea from biomedical signals using artificial intelligence techniques: Methods, challenges, and future works

Moridian, P Shoeibi, A Khodatars, M Jafari, M Pachori, RB Khadem, A Alizadehsani, R Ling, SH

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Publisher:: WILEY PERIODICALS, INC
Publication Type:: Journal Article
Citation:: Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2022, 12, (6)
Issue Date:: 2022-11-01

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Field	Value	Language
dc.contributor.author	Moridian, P
dc.contributor.author	Shoeibi, A
dc.contributor.author	Khodatars, M
dc.contributor.author	Jafari, M
dc.contributor.author	Pachori, RB
dc.contributor.author	Khadem, A
dc.contributor.author	Alizadehsani, R
dc.contributor.author	Ling, SH
dc.date.accessioned	2023-07-04T02:11:09Z
dc.date.available	2023-07-04T02:11:09Z
dc.date.issued	2022-11-01
dc.identifier.citation	Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2022, 12, (6)
dc.identifier.issn	1942-4787
dc.identifier.issn	1942-4795
dc.identifier.uri	http://hdl.handle.net/10453/171172
dc.description.abstract	Apnea is a sleep disorder that stops or reduces airflow for a short time during sleep. Sleep apnea may last for a few seconds and happen for many while sleeping. This reduction in breathing is associated with loud snoring, which may awaken the person with a feeling of suffocation. So far, a variety of methods have been introduced by researchers to diagnose sleep apnea, among which the polysomnography (PSG) method is known to be the best. Analysis of PSG signals is very complicated. Many studies have been conducted on the automatic diagnosis of sleep apnea from biological signals using artificial intelligence (AI), including machine learning (ML) and deep learning (DL) methods. This research reviews and investigates the studies on the diagnosis of sleep apnea using AI methods. First, computer aided diagnosis system (CADS) for sleep apnea using ML and DL techniques along with its parts including dataset, preprocessing, and ML and DL methods are introduced. This research also summarizes the important specifications of the studies on the diagnosis of sleep apnea using ML and DL methods in a table. In the following, a comprehensive discussion is made on the studies carried out in this field. The challenges in the diagnosis of sleep apnea using AI methods are of paramount importance for researchers. Accordingly, these obstacles are elaborately addressed. In another section, the most important future works for studies on sleep apnea detection from PSG signals and AI techniques are presented. Ultimately, the essential findings of this study are provided in the conclusion section. This article is categorized under: Technologies > Artificial Intelligence Application Areas > Data Mining Software Tools Algorithmic Development > Biological Data Mining.
dc.language	English
dc.publisher	WILEY PERIODICALS, INC
dc.relation.ispartof	Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
dc.relation.isbasedon	10.1002/widm.1478
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0804 Data Format, 0806 Information Systems
dc.subject.classification	4605 Data management and data science
dc.title	Automatic diagnosis of sleep apnea from biomedical signals using artificial intelligence techniques: Methods, challenges, and future works
dc.type	Journal Article
utslib.citation.volume	12
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0804 Data Format
utslib.for	0806 Information Systems
utslib.copyright.status	open_access	*
dc.date.updated	2023-07-04T02:11:06Z
pubs.issue	6
pubs.publication-status	Published
pubs.volume	12
utslib.citation.issue	6

Abstract:

Apnea is a sleep disorder that stops or reduces airflow for a short time during sleep. Sleep apnea may last for a few seconds and happen for many while sleeping. This reduction in breathing is associated with loud snoring, which may awaken the person with a feeling of suffocation. So far, a variety of methods have been introduced by researchers to diagnose sleep apnea, among which the polysomnography (PSG) method is known to be the best. Analysis of PSG signals is very complicated. Many studies have been conducted on the automatic diagnosis of sleep apnea from biological signals using artificial intelligence (AI), including machine learning (ML) and deep learning (DL) methods. This research reviews and investigates the studies on the diagnosis of sleep apnea using AI methods. First, computer aided diagnosis system (CADS) for sleep apnea using ML and DL techniques along with its parts including dataset, preprocessing, and ML and DL methods are introduced. This research also summarizes the important specifications of the studies on the diagnosis of sleep apnea using ML and DL methods in a table. In the following, a comprehensive discussion is made on the studies carried out in this field. The challenges in the diagnosis of sleep apnea using AI methods are of paramount importance for researchers. Accordingly, these obstacles are elaborately addressed. In another section, the most important future works for studies on sleep apnea detection from PSG signals and AI techniques are presented. Ultimately, the essential findings of this study are provided in the conclusion section. This article is categorized under: Technologies > Artificial Intelligence Application Areas > Data Mining Software Tools Algorithmic Development > Biological Data Mining.

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http://hdl.handle.net/10453/171172