Monitoring of nocturnal central sleep apnea in Heart failure patients using noncontact respiratory differences

Khushaba, RN; Armitstead, J; Schindhelm, K

Monitoring of nocturnal central sleep apnea in Heart failure patients using noncontact respiratory differences

Khushaba, RN

Armitstead, J Schindhelm, K

Permalink

Publication Type:: Conference Proceeding
Citation:: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2017, pp. 1534 - 1538
Issue Date:: 2017-09-13

Closed Access

	Filename	Description	Size
	08037128.pdf	Accepted Manuscript version	1.09 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Khushaba, RN https://orcid.org/0000-0001-8528-8979	en_US
dc.contributor.author	Armitstead, J	en_US
dc.contributor.author	Schindhelm, K	en_US
dc.date.issued	2017-09-13	en_US
dc.identifier.citation	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2017, pp. 1534 - 1538	en_US
dc.identifier.isbn	9781509028092	en_US
dc.identifier.issn	1557-170X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/125882
dc.description.abstract	© 2017 IEEE. Monitoring of respiration patterns allows the early detection of various breathing disorders and may better identify those at risk for adverse acute outcomes in a variety of clinical settings. In this paper, we report on the use of SleepMinder (SM), a bedside non-contact Doppler-based biomotion recording sensor, to monitor remotely the nocturnal respiration patterns of 50 patients with systolic Heart failure (HF) while undergoing a lab based Polysomnography (PSG) test. A new respiration rate (RR) monitoring algorithm was developed based on the collected overnight radar signals. Two schemes of RR scoring were utilized: respiratory rate count (RRC) and instantaneous respiratory rates (IRR). Analysis of SM vs. PSG revealed that the mean/median IRR scored by SM is highly correlated with that scored on the nasal flow/effort signals from the corresponding PSG studies on all patients, with a significant correlation coefficient of 0.98 (average absolute difference of 0.31 breaths/min), and 0.97 (p<0.01, average absolute difference of 0.38 breaths/min) for the median and mean of RR respectively. Our experimental results also show that the difference between the RR estimations from IRR and RRC schemes can be utilized to identify central sleep apnea (CSA)/Cheyne-Stokes respiration (CSR) sections without additional apnea detection modules. As a result, with a sensitivity and specificity of 71% and 88% respectively, and an accuracy of 86%, our CSA/CSR screener, plugged with our RR estimation, can play an important role in the remote management of HF patients.	en_US
dc.relation.ispartof	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS	en_US
dc.relation.isbasedon	10.1109/EMBC.2017.8037128	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Sleep Apnea, Central	en_US
dc.subject.mesh	Cheyne-Stokes Respiration	en_US
dc.subject.mesh	Polysomnography	en_US
dc.subject.mesh	Heart Failure	en_US
dc.subject.mesh	Respiratory Rate	en_US
dc.title	Monitoring of nocturnal central sleep apnea in Heart failure patients using noncontact respiratory differences	en_US
dc.type	Conference Proceeding
utslib.for	0903 Biomedical Engineering	en_US
pubs.embargo.period	Not known	en_US
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/Faculty of Engineering and Information Technology/School of Biomedical Engineering
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US

Abstract:

© 2017 IEEE. Monitoring of respiration patterns allows the early detection of various breathing disorders and may better identify those at risk for adverse acute outcomes in a variety of clinical settings. In this paper, we report on the use of SleepMinder (SM), a bedside non-contact Doppler-based biomotion recording sensor, to monitor remotely the nocturnal respiration patterns of 50 patients with systolic Heart failure (HF) while undergoing a lab based Polysomnography (PSG) test. A new respiration rate (RR) monitoring algorithm was developed based on the collected overnight radar signals. Two schemes of RR scoring were utilized: respiratory rate count (RRC) and instantaneous respiratory rates (IRR). Analysis of SM vs. PSG revealed that the mean/median IRR scored by SM is highly correlated with that scored on the nasal flow/effort signals from the corresponding PSG studies on all patients, with a significant correlation coefficient of 0.98 (average absolute difference of 0.31 breaths/min), and 0.97 (p<0.01, average absolute difference of 0.38 breaths/min) for the median and mean of RR respectively. Our experimental results also show that the difference between the RR estimations from IRR and RRC schemes can be utilized to identify central sleep apnea (CSA)/Cheyne-Stokes respiration (CSR) sections without additional apnea detection modules. As a result, with a sensitivity and specificity of 71% and 88% respectively, and an accuracy of 86%, our CSA/CSR screener, plugged with our RR estimation, can play an important role in the remote management of HF patients.

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

http://hdl.handle.net/10453/125882