A generalized algorithm for the automatic diagnosis of sleep apnea from per-sample encoding of airflow and oximetry.

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

A generalized algorithm for the automatic diagnosis of sleep apnea from per-sample encoding of airflow and oximetry.

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

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Publisher:: IOP Publishing Ltd
Publication Type:: Journal Article
Citation:: Physiol Meas, 2022, 43, (6)
Issue Date:: 2022-06-30

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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	2023-03-27T00:17:16Z
dc.date.available	2022-04-27
dc.date.available	2023-03-27T00:17:16Z
dc.date.issued	2022-06-30
dc.identifier.citation	Physiol Meas, 2022, 43, (6)
dc.identifier.issn	0967-3334
dc.identifier.issn	1361-6579
dc.identifier.uri	http://hdl.handle.net/10453/168494
dc.description.abstract	Objective. Sleep apnea is a common sleep breathing disorder that can significantly decrease sleep quality and have major health consequences. It is diagnosed based on the apnea hypopnea index (AHI). This study explored a novel, generalized algorithm for the automatic diagnosis of sleep apnea employing airflow (AF) and oximetry (SpO2) signals.Approach. Of the 988 polysomnography records, 45 were randomly selected for developing the automatic algorithm and the remainder 943 for validating purposes. The algorithm detects apnea events by a per-sample encoding process applied to the peak excursion of AF signal. Hypopnea events were detected from the per-sample encoding of AF and SpO2with an adjustment to time lag in SpO2. Total recording time was automatically processed and optimized for computation of total sleep time (TST). Total number of detected events and computed TST were used to estimate AHI. The estimated AHI was validated against the scored data from the Sleep Heart Health Study.Main results. Intraclass correlation coefficient of 0.94 was obtained between estimated and scored AHIs. The diagnostic accuracies were 93.5%, 92.4%, and 96.6% for AHI cut-off values of ≥5, ≥15, and ≥30 respectively. The overall accuracy for the combined severity categories (normal, mild, moderate, and severe) and kappa were 83.4% and 0.77 respectively.Significance. This new automatic technique was found to be superior to the other existing methods and can be applied to any portable sleep devices especially for home sleep apnea tests.
dc.format	Electronic
dc.language	eng
dc.publisher	IOP Publishing Ltd
dc.relation.ispartof	Physiol Meas
dc.relation.isbasedon	10.1088/1361-6579/ac6b11
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	Respiration
dc.subject.mesh	Sleep Apnea Syndromes
dc.subject.mesh	Sleep Apnea, Obstructive
dc.subject.mesh	Sleep Wake Disorders
dc.subject.mesh	Humans
dc.subject.mesh	Sleep Apnea Syndromes
dc.subject.mesh	Sleep Apnea, Obstructive
dc.subject.mesh	Oximetry
dc.subject.mesh	Respiration
dc.subject.mesh	Algorithms
dc.subject.mesh	Sleep Wake Disorders
dc.subject.mesh	Algorithms
dc.subject.mesh	Humans
dc.subject.mesh	Oximetry
dc.subject.mesh	Respiration
dc.subject.mesh	Sleep Apnea Syndromes
dc.subject.mesh	Sleep Apnea, Obstructive
dc.subject.mesh	Sleep Wake Disorders
dc.title	A generalized algorithm for the automatic diagnosis of sleep apnea from per-sample encoding of airflow and oximetry.
dc.type	Journal Article
utslib.citation.volume	43
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	*
dc.date.updated	2023-03-27T00:17:15Z
pubs.issue	6
pubs.publication-status	Published online
pubs.volume	43
utslib.citation.issue	6

Abstract:

Objective. Sleep apnea is a common sleep breathing disorder that can significantly decrease sleep quality and have major health consequences. It is diagnosed based on the apnea hypopnea index (AHI). This study explored a novel, generalized algorithm for the automatic diagnosis of sleep apnea employing airflow (AF) and oximetry (SpO2) signals.Approach. Of the 988 polysomnography records, 45 were randomly selected for developing the automatic algorithm and the remainder 943 for validating purposes. The algorithm detects apnea events by a per-sample encoding process applied to the peak excursion of AF signal. Hypopnea events were detected from the per-sample encoding of AF and SpO2with an adjustment to time lag in SpO2. Total recording time was automatically processed and optimized for computation of total sleep time (TST). Total number of detected events and computed TST were used to estimate AHI. The estimated AHI was validated against the scored data from the Sleep Heart Health Study.Main results. Intraclass correlation coefficient of 0.94 was obtained between estimated and scored AHIs. The diagnostic accuracies were 93.5%, 92.4%, and 96.6% for AHI cut-off values of ≥5, ≥15, and ≥30 respectively. The overall accuracy for the combined severity categories (normal, mild, moderate, and severe) and kappa were 83.4% and 0.77 respectively.Significance. This new automatic technique was found to be superior to the other existing methods and can be applied to any portable sleep devices especially for home sleep apnea tests.

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http://hdl.handle.net/10453/168494