Non-Contact Heartbeat and Respiration Signal Detection Based on Improved Variational Mode Extraction

Yujie Zhou; Chengyan Lin; Qinwei Ni; Yusheng Yuan; Huabin He; Zhiming Cai

doi:10.1109/ACCESS.2024.3434952

IEEE Access (Jan 2024)

Non-Contact Heartbeat and Respiration Signal Detection Based on Improved Variational Mode Extraction

Yujie Zhou,
Chengyan Lin,
Qinwei Ni,
Yusheng Yuan,
Huabin He,
Zhiming Cai

Affiliations

Yujie Zhou: School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou, China
Chengyan Lin: ORCiD; School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou, China
Qinwei Ni: School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou, China
Yusheng Yuan: School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou, China
Huabin He: School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou, China
Zhiming Cai: ORCiD; School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou, China

DOI: https://doi.org/10.1109/ACCESS.2024.3434952
Journal volume & issue: Vol. 12
pp. 106550 – 106566

Abstract

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Vital signs, such as heart rate (HR) and respiration rate (RR) are essential indicators of human body function and health. Most medical systems for estimating HR and RR necessitate direct contact with the human body, which potentially causes discomfort and imposes unnecessary medical burdens, particularly in long-term monitoring cases. The overwhelming clutter in the radar field of view drowns out the cardiopulmonary signals, making them difficult to distinguish in the surrounding noise. Moreover, the chest wall vibration caused by the heartbeat is much smaller when dyspnea occurs, and interference from respiratory harmonics is unavoidable, making it difficult to estimate heart rate accurately. We present an HR and RR monitoring method for accurate non-contact vital signs (NCVS) detection and better privacy protection using frequency-modulated continuous wave (FMCW) radar to alleviate the dilemma. Firstly, the vital sign signals are obtained by removing the static clutter noise in the background. Secondly, the cardiopulmonary signal is extracted through enhanced differentiate and cross-multiplication to overcome phase discontinuity. Thirdly, the respiration and heartbeat waveforms are extracted from the cardiopulmonary signal with improved variational mode extraction. Ultimately, the sparse respiration and heartbeat spectrum is constructed to estimate RR and HR. The performance of the proposed method is evaluated across comprehensive experimental scenarios including user diversity, varying distances, different angles, and subject orientations to the radar sensor. Experimental results indicate that the proposed method can reduce the clutter noise and suppress the interference of the respiration harmonics. By eliminating the interference of different decomposition components, the accuracy of RR and HR estimation is superior to the existing studies.

Published in IEEE Access

ISSN: 2169-3536 (Online)
Publisher: IEEE
Country of publisher: United States
LCC subjects: Technology: Electrical engineering. Electronics. Nuclear engineering
Website: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639

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