Non-contact Doppler radar based prediction of nocturnal body orientations using deep neural network for chronic heart failure patients

Tran, VP; Al-Jumaily, AA

Non-contact Doppler radar based prediction of nocturnal body orientations using deep neural network for chronic heart failure patients

Tran, VP

Al-Jumaily, AA

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Publication Type:: Conference Proceeding
Citation:: 2017 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2017, 2017, 2018-January pp. 1 - 5
Issue Date:: 2017-06-28

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Field	Value	Language
dc.contributor.author	Tran, VP https://orcid.org/0000-0001-9152-3875	en_US
dc.contributor.author	Al-Jumaily, AA https://orcid.org/0000-0003-0297-2463	en_US
dc.date.issued	2017-06-28	en_US
dc.identifier.citation	2017 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2017, 2017, 2018-January pp. 1 - 5	en_US
dc.identifier.isbn	9781538608722	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132939
dc.description.abstract	© 2017 IEEE. Sleep is crucial in our daily life as it plays a key role in our physical and mental health. It is important to monitor the sleep body orientations and movements due to its relationships to particular diseases, e.g., obstructive sleep apnea, insomnia or periodic limb movement disorder. Analyzing sleep body orientations also helps in determining sleep quality and irregular sleeping patterns. However, the current non-invasive sleep body orientations monitoring technologies are not well suited for long-term continuous monitoring due to its restrictions in mobility and comfort. This paper proposes a system that applies a features extraction process, utilizing wavelet packet decomposition, to extract features that describe the non-contact Doppler radar signatures caused by the body orientations. A database consisting of 24 chronic heart failure patients is selected for the training, validation and test of the non-contact body orientations prediction. These patients are diagnosed with New York Heart Association heart failure classification Class II & III and underwent full polysomnography analysis for the diagnosis of sleep apnea, disordered sleep, or both. The patients' data are randomly concatenated and partitioned into the ratio of 50% for 'Training', 15% for 'Validation' and 35% for 'Test. Across the 'Test dataset with total sleep duration of 65 hours, the body orientations prediction accuracy achieved a correct classification rate of 99.2% for 5 classes of 'Prone', 'Upright', 'Supine', 'Right and 'Left body orientations. The misclassification rate is 0.8%. A potential application would be non-contact continuous monitoring of nocturnal body orientations in the home.	en_US
dc.relation.ispartof	2017 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2017	en_US
dc.relation.isbasedon	10.1109/ICECTA.2017.8252020	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Non-contact Doppler radar based prediction of nocturnal body orientations using deep neural network for chronic heart failure patients	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2018-January	en_US
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
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	2018-January	en_US

Abstract:

© 2017 IEEE. Sleep is crucial in our daily life as it plays a key role in our physical and mental health. It is important to monitor the sleep body orientations and movements due to its relationships to particular diseases, e.g., obstructive sleep apnea, insomnia or periodic limb movement disorder. Analyzing sleep body orientations also helps in determining sleep quality and irregular sleeping patterns. However, the current non-invasive sleep body orientations monitoring technologies are not well suited for long-term continuous monitoring due to its restrictions in mobility and comfort. This paper proposes a system that applies a features extraction process, utilizing wavelet packet decomposition, to extract features that describe the non-contact Doppler radar signatures caused by the body orientations. A database consisting of 24 chronic heart failure patients is selected for the training, validation and test of the non-contact body orientations prediction. These patients are diagnosed with New York Heart Association heart failure classification Class II & III and underwent full polysomnography analysis for the diagnosis of sleep apnea, disordered sleep, or both. The patients' data are randomly concatenated and partitioned into the ratio of 50% for 'Training', 15% for 'Validation' and 35% for 'Test. Across the 'Test dataset with total sleep duration of 65 hours, the body orientations prediction accuracy achieved a correct classification rate of 99.2% for 5 classes of 'Prone', 'Upright', 'Supine', 'Right and 'Left body orientations. The misclassification rate is 0.8%. A potential application would be non-contact continuous monitoring of nocturnal body orientations in the home.

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http://hdl.handle.net/10453/132939