Separation of Doppler radar-based respiratory signatures

Lee, Yee Siong, Pathirana, Pubudu N., Evans, Robin J. and Steinfort, Christopher L. 2016, Separation of Doppler radar-based respiratory signatures, Medical and biological engineering and computing, vol. 54, no. 8, pp. 1169-1179, doi: 10.1007/s11517-015-1379-3.

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Title Separation of Doppler radar-based respiratory signatures
Author(s) Lee, Yee Siong
Pathirana, Pubudu N.ORCID iD for Pathirana, Pubudu N.
Evans, Robin J.
Steinfort, Christopher L.
Journal name Medical and biological engineering and computing
Volume number 54
Issue number 8
Start page 1169
End page 1179
Total pages 11
Publisher Springer
Place of publication Berlin, Germany
Publication date 2016-08
ISSN 0140-0118
Keyword(s) Doppler radar
Respiration pattern
Source separation
Summary Respiration detection using microwave Doppler radar has attracted significant interest primarily due to its unobtrusive form of measurement. With less preparation in comparison with attaching physical sensors on the body or wearing special clothing, Doppler radar for respiration detection and monitoring is particularly useful for long-term monitoring applications such as sleep studies (i.e. sleep apnoea, SIDS). However, motion artefacts and interference from multiple sources limit the widespread use and the scope of potential applications of this technique. Utilising the recent advances in independent component analysis (ICA) and multiple antenna configuration schemes, this work investigates the feasibility of decomposing respiratory signatures into each subject from the Doppler-based measurements. Experimental results demonstrated that FastICA is capable of separating two distinct respiratory signatures from two subjects adjacent to each other even in the presence of apnoea. In each test scenario, the separated respiratory patterns correlate closely to the reference respiration strap readings. The effectiveness of FastICA in dealing with the mixed Doppler radar respiration signals confirms its applicability in healthcare applications, especially in long-term home-based monitoring as it usually involves at least two people in the same environment (i.e. two people sleeping next to each other). Further, the use of FastICA to separate involuntary movements such as the arm swing from the respiratory signatures of a single subject was explored in a multiple antenna environment. The separated respiratory signal indeed demonstrated a high correlation with the measurements made by a respiratory strap used currently in clinical settings.
Language eng
DOI 10.1007/s11517-015-1379-3
Field of Research 090609 Signal Processing
090303 Biomedical Instrumentation
090399 Biomedical Engineering not elsewhere classified
Socio Economic Objective 920115 Respiratory System and Diseases (incl. Asthma)
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, Springer
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