Vital Signs Detection Based on Variational Modal Decomposition in Resting State

doi:10.3778/j.issn.1002-8331.2011-0486

Abstract

Abstract: Traditional wireless vital signs detection methods are prone to residual harmonics in the separation of heartbeat and breathing signals. In response to this situation, a vital signal detection method based on variational modal decomposition（VMD） is proposed. This method uses millimeter-band frequency modulated continuous wave（FMCW） radar to obtain vital signs signals. According to the frequency characteristics of heartbeat and respiration, the main signal is decomposed into different modes including more complete respiration and heartbeat signals without harmonic residuals by using the VMD algorithm to ensure the frequency range between each mode does not overlap with each other. Experimental results show that the proposed algorithm can effectively extract the breathing and heartbeat signals　of target. Compared with the traditional modal decomposition algorithm, it has higher robustness and stability, has a good signal-to-noise ratio（SNR）, and improves measurement accuracy and distance.

Key words: millimeter wave, frequency modulated continuous wave, modal decomposition, vital signs detection, variational modal decomposition

摘要： 传统的无线生命体征监测方法在心跳和呼吸信号的分离方面容易存在谐波残留现象，针对这一情况，提出了一种基于变分模态分解（VMD）的生命信号检测方法。该方法使用毫米波段调频连续波（FMCW）雷达进行生命体征信号获取，根据心跳及呼吸的频率特征，使用VMD算法将主要信号分解为不同模态，保证了各模态之间信号频率范围互不重叠，分离出较为完整且无谐波残留的呼吸及心跳信号。实验结果表明，所提算法能够有效提取出目标的呼吸及心跳信号，且相比传统的模态分解算法具有更高的鲁棒性和稳定性，具有良好的信噪比（SNR），提高了测量精度和距离。

关键词: 毫米波, 调频连续波, 模态分解, 生命体征检测, 变分模态分解

AN Meichen, WANG Peng, CAI Chao, PENG Kai. Vital Signs Detection Based on Variational Modal Decomposition in Resting State[J]. Computer Engineering and Applications, 2022, 58(9): 288-293.

安美晨, 王鹏, 蔡超, 彭凯. 静息态下基于变分模态分解的生命体征检测[J]. 计算机工程与应用, 2022, 58(9): 288-293.

References

[1] VGONTZAS A N，PAPANICOLAOU D A，BIXLER E O，et al.Sleep apnea and daytime sleepiness and fatigue：relation to visceral obesity，insulin resistance，and hypercytokinemia[J].The Journal of Clinical Endocrinology and Metabolism，2000，85（3）：1151-1158.
[2] BRAUN P X，GMACHL C F，DWEIK R A.Bridging the collaborative gap：realizing the clinical potential of breath analysis for disease diagnosis and monitoring-tutorial[J].IEEE Sensors Journal，2012，12（11）：3258-3270.
[3] CHUNG G S，CHOI B H，KIM K K，et al.REM sleep classification with respiration rates[C]//2007 6th International Special Topic Conference on Information Technology Applications in Biomedicine，2007：194-197.
[4] KUSHIDA C A，LITTNER M R，MORGENTHALER T，et al.Practice parameters for the indications for polysomnography and related procedures：an update for 2005[J].Sleep，2005，28（4）：499-523.
[5] ABDELNASSER H，HARRAS K A，YOUSSEF M.UbiBreathe：a ubiquitous non-invasive WiFi-based breathing estimator[C]//Proceedings of the 16th ACM International Symposium on Mobile Ad Hoc Networking and Computing，2015：277-286.
[6] RAVICHANDRAN R，SABA E，CHEN K Y，et al.WiBreathe：estimating respiration rate using wireless signals in natural settings in the home[C]//2015 IEEE International Conference on Pervasive Computing and Communications（PerCom），2015：131-139.
[7] PATWARI N，BREWER L，TATE Q，et al.Breathfinding：a wireless network that monitors and locates breathing in a home[J].IEEE Journal of Selected Topics in Signal Processing，2014，8（1）：30-42.
[8] LIU J，WANG Y，CHEN Y，et al.Tracking vital signs during sleep leveraging off-the-shelf WiFi[C]//Proceedings of the 16th ACM International Symposium on Mobile Ad Hoc Networking and Computing，2015：267-276.
[9] LEE Y，CHOI J W，CHO S H.Vital sign quality assessment based on IR-UWB radar sensor[C]//2017 International Conference on Information and Communication Technology Convergence（ICTC），2017：896-900.
[10] LEE H，KIM B H，PARK J K，et al.A novel vital-sign sensing algorithm for multiple subjects based on 24-GHz FMCW Doppler radar[J].Remote Sensing，2019，11（10）：1237.
[11] ZHANG D，KURATA M，INABA T.FMCW radar for small displacement detection of vital signal using projection matrix method[J].International Journal of Antennas and Propagation，2013：1-5.
[12] BUDGE M C，BURT M P.Range correlation effects in radars[C]//The Record of the 1993 IEEE National Radar Conference，1993：212-216.
[13] ADIB F，MAO H，KABELAC Z，et al.Smart homes that monitor breathing and heart rate[C]//Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems，2015：837-846.
[14] YANG Z，PATHAK P H，ZENG Y，et al.Monitoring vital signs using millimeter wave[C]//Proceedings of the 17th ACM International Symposium on Mobile Ad Hoc Networking and Computing，2016：211-220.
[15] BAKHTIARI S，ELMER T W，COX N M，et al.Compact millimeter-wave sensor for remote monitoring of vital signs[J].IEEE Transactions on Instrumentation and Measurement，2012，61（3）：830-841.
[16] ALIZADEH M，SHAKER G，ALMEIDA J C M D，et al.Remote monitoring of human vital signs using mm-wave FMCW radar[J].IEEE Access，2019，7：54958-54968.
[17] DRAGOMIRETSKIY K，ZOSSO D.Variational mode decomposition[J].IEEE Transactions on Signal Processing，2014，62（3）：531-544.
[18] WANG Y，MARKERT R，XIANG J，et al.Research on variational mode decomposition and its application in detecting rub-impact fault of the rotor system[J].Mechanical Systems and Signal Processing，2015，60：243-251.
[19] PARADISO C，COLINO F，LIU S.The validity and reliability of the MiBand wearable device for measuring steps and heart rate[J].International Journal of Exercise Science，2020，13（4）：689-701.