Applying composite physiological characteristics to assess the severity of obstructive sleep apnea

Lee, Chun‐Chia; Wang, Chien-Po; Chiang, Hsiu‐Sen; Liu, Jingwei; Chen, Hsuan-Chun

doi:10.1007/s12652-020-02493-y

Cited by 1 publication

(1 citation statement)

References 36 publications

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“…In C.‐C. Lee et al (2020) and Vaquerizo‐Villar et al (2017), various time‐domain and statistical features are used for sleep apnea detection, and satisfactory results have been obtained. In Table 3, a summary of other papers are provided where they have applied the time‐domain and statistical feature extraction methods for sleep apnea detection.…”

Section: Cads Based On Ai Methods For Sleep Apnea Syndrome Detectionmentioning

confidence: 99%

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

Moridian

Shoeibi

Khodatars

et al. 2022

WIREs Data Min & Knowl

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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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Section: Cads Based On Ai Methods For Sleep Apnea Syndrome Detectionmentioning

confidence: 99%

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

Moridian

Shoeibi

Khodatars

et al. 2022

WIREs Data Min & Knowl

View full text Add to dashboard Cite

show abstract

Applying composite physiological characteristics to assess the severity of obstructive sleep apnea

Cited by 1 publication

References 36 publications

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

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

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