A novel algorithm for automatic diagnosis of sleep apnea from airflow and oximetry signals.

Uddin, MB; Chow, CM; Ling, SH; Su, SW

A novel algorithm for automatic diagnosis of sleep apnea from airflow and oximetry signals.

Uddin, MB Chow, CM Ling, SH Su, SW

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Publisher:: IOP PUBLISHING LTD
Publication Type:: Journal Article
Citation:: Physiological measurement, 2021, 42, (1), pp. 015001-015001
Issue Date:: 2021-02-06

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Field	Value	Language
dc.contributor.author	Uddin, MB
dc.contributor.author	Chow, CM
dc.contributor.author	Ling, SH
dc.contributor.author	Su, SW
dc.date.accessioned	2022-01-31T01:55:36Z
dc.date.available	2022-01-31T01:55:36Z
dc.date.issued	2021-02-06
dc.identifier.citation	Physiological measurement, 2021, 42, (1), pp. 015001-015001
dc.identifier.issn	0967-3334
dc.identifier.issn	1361-6579
dc.identifier.uri	http://hdl.handle.net/10453/153908
dc.description.abstract	<h4>Objective</h4>Sleep apnea significantly decreases the quality of life. The apnea hypopnea index (AHI) is the main indicator for sleep apnea diagnosis. This study explored a novel automatic algorithm to diagnose sleep apnea from nasal airflow (AF) and pulse oximetry (SpO<sub>2</sub>) signals.<h4>Approach</h4>Of the 988 polysomnography (PSG) records from the sleep heart health study (SHHS), 45 were randomly selected for the development of an algorithm and the remainder for validation (n = 943). The algorithm detects apnea events by a digitization process, following the determination of the peak excursion (peak-to-trough amplitude) from AF envelope. Hypopnea events were determined from the AF envelope and oxygen desaturation with correction to time lag in SpO<sub>2</sub>. Total sleep time (TST) was estimated from an optimized percentage of artefact-free total recording time. AHI was estimated from the number of detected events divided by the estimated TST. The estimated AHI was compared to the scored SHHS data for performance evaluation.<h4>Main results</h4>The validation showed good agreement between the estimated and scored AHI (intraclass correlation coefficient of 0.95 and mean ±95% limits of agreement of -1.6 ±12.5 events h<sup>-1</sup>). The diagnostic accuracies were found: 90.7%, 91%, and 96.7% for AHI cut-off ≥5, ≥15, and ≥30 respectively.<h4>Significance</h4>The new algorithm is accurate over other existing methods for the automatic diagnosis of sleep apnea. It is applicable to any portable sleep screeners especially for the home diagnosis of sleep apnea.
dc.format	Electronic
dc.language	eng
dc.publisher	IOP PUBLISHING LTD
dc.relation.ispartof	Physiological measurement
dc.relation.isbasedon	10.1088/1361-6579/abd238
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0903 Biomedical Engineering, 0906 Electrical and Electronic Engineering, 1116 Medical Physiology
dc.subject.classification	Biomedical Engineering
dc.subject.mesh	Algorithms
dc.subject.mesh	Humans
dc.subject.mesh	Oximetry
dc.subject.mesh	Quality of Life
dc.subject.mesh	Sleep Apnea Syndromes
dc.subject.mesh	Sleep Apnea, Obstructive
dc.subject.mesh	Humans
dc.subject.mesh	Sleep Apnea Syndromes
dc.subject.mesh	Sleep Apnea, Obstructive
dc.subject.mesh	Oximetry
dc.subject.mesh	Algorithms
dc.subject.mesh	Quality of Life
dc.title	A novel algorithm for automatic diagnosis of sleep apnea from airflow and oximetry signals.
dc.type	Journal Article
utslib.citation.volume	42
utslib.location.activity	England
utslib.for	0903 Biomedical Engineering
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1116 Medical Physiology
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Centre for Health Technologies (CHT)
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-01-31T01:55:35Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	42
utslib.citation.issue	1

Abstract:

Objective

Sleep apnea significantly decreases the quality of life. The apnea hypopnea index (AHI) is the main indicator for sleep apnea diagnosis. This study explored a novel automatic algorithm to diagnose sleep apnea from nasal airflow (AF) and pulse oximetry (SpO₂) signals.

Approach

Of the 988 polysomnography (PSG) records from the sleep heart health study (SHHS), 45 were randomly selected for the development of an algorithm and the remainder for validation (n = 943). The algorithm detects apnea events by a digitization process, following the determination of the peak excursion (peak-to-trough amplitude) from AF envelope. Hypopnea events were determined from the AF envelope and oxygen desaturation with correction to time lag in SpO₂. Total sleep time (TST) was estimated from an optimized percentage of artefact-free total recording time. AHI was estimated from the number of detected events divided by the estimated TST. The estimated AHI was compared to the scored SHHS data for performance evaluation.

Main results

The validation showed good agreement between the estimated and scored AHI (intraclass correlation coefficient of 0.95 and mean ±95% limits of agreement of -1.6 ±12.5 events h^-1). The diagnostic accuracies were found: 90.7%, 91%, and 96.7% for AHI cut-off ≥5, ≥15, and ≥30 respectively.

Significance

The new algorithm is accurate over other existing methods for the automatic diagnosis of sleep apnea. It is applicable to any portable sleep screeners especially for the home diagnosis of sleep apnea.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/153908