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Proceedings Paper

Remote heartbeat signal detection from visible spectrum recordings based on blind deconvolution
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Paper Abstract

While recent advances have shown that it is possible to acquire a signal equivalent to the heartbeat from visual spectrum video recordings of the human skin, extracting the heartbeat’s exact timing information from it, for the purpose of heart rate variability analysis, remains a challenge. In this paper, we explore two novel methods to estimate the remote cardiac signal peak positions, aiming at a close representation of the R-peaks of the ECG signal. The first method is based on curve fitting (CF) using a modified filtered least mean square (LMS) optimization and the second method is based on system estimation using blind deconvolution (BDC). To prove the efficacy of the developed algorithms, we compared results obtained with the ground truth (ECG) signal. Both methods achieved a low relative error between the peaks of the two signals. This work, performed under an IRB approved protocol, provides initial proof that blind deconvolution techniques can be used to estimate timing information of the cardiac signal closely correlated to the one obtained by traditional ECG. The results show promise for further development of a remote sensing of cardiac signals for the purpose of remote vital sign and stress detection for medical, security, military and civilian applications.

Paper Details

Date Published: 19 May 2016
PDF: 9 pages
Proc. SPIE 9871, Sensing and Analysis Technologies for Biomedical and Cognitive Applications 2016, 987103 (19 May 2016); doi: 10.1117/12.2223933
Show Author Affiliations
Balvinder Kaur, U.S. Army Research, Development and Engineering Command (United States)
George Mason Univ. (United States)
Sophia Moses, George Mason Univ. (United States)
Megha Luthra, George Mason Univ. (United States)
Vasiliki N. Ikonomidou, George Mason Univ. (United States)

Published in SPIE Proceedings Vol. 9871:
Sensing and Analysis Technologies for Biomedical and Cognitive Applications 2016
Liyi Dai; Yufeng Zheng; Henry Chu; Anke D. Meyer-Bäse, Editor(s)

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