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Application of artificial intelligence in the diagnosis of sleep apnea

Bazoukis, George, Bollepalli, Sandeep Chandra, Chung, Cheuk To, Li, Xinmu, Tse, Gary, Bartley, Bethany L., Batool-Anwar, Salma, Quan, Stuart F., Armoundas, Antonis A. (2023) Application of artificial intelligence in the diagnosis of sleep apnea. Journal of Clinical Sleep Medicine, 19 (7). pp. 1337-1363. ISSN 1550-9397. (doi:10.5664/jcsm.10532) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:100401)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL:
https://doi.org/10.5664/jcsm.10532

Abstract

STUDY OBJECTIVES: Machine learning (ML) models have been employed in the setting of sleep disorders. This review aims to summarize the existing data about the role of ML techniques in the diagnosis, classification, and treatment of sleep related breathing disorders.

METHODS: A systematic search in MedLine, EMBASE, and Cochrane databases through January 2022 was performed.

RESULTS: Our search strategy revealed 132 studies that were included in the systematic review. Existing data show that ML models have been successfully used for diagnostic purposes. Specifically, ML models showed good performance in diagnosing sleep apnea using easily obtained features from the electrocardiogram, pulse oximetry and sound signals. Similarly, ML showed good performance for the classification of sleep apnea into obstructive and central categories, as well as predicting apnea severity. Existing data show promising results for the ML-based guided treatment of sleep apnea. Specifically, the prediction of outcomes following surgical treatment and optimization of continuous positive airway pressure therapy, can be guided by ML models.

CONCLUSIONS: The adoption and implementation of ML in the field of sleep related breathing disorders is promising. Advancements in wearable sensor technology and ML models can help clinicians predict, diagnose and classify sleep apnea more accurately and efficiently.

Item Type: Article
DOI/Identification number: 10.5664/jcsm.10532
Uncontrolled keywords: Neurology (clinical), Neurology, Pulmonary and Respiratory Medicine
Subjects: R Medicine
Divisions: Divisions > Division of Natural Sciences > Kent and Medway Medical School
SWORD Depositor: JISC Publications Router
Depositing User: JISC Publications Router
Date Deposited: 10 Mar 2023 10:07 UTC
Last Modified: 11 Jul 2023 13:48 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/100401 (The current URI for this page, for reference purposes)

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