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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2020, Vol. 54 Issue (10): 2058-2066    DOI: 10.3785/j.issn.1008-973X.2020.10.024
    
Ballistocardiogram and respiratory signal separation based on variational mode decomposition
Ji-jun TONG1(),Yan-jie BAI1,Jian-wei PAN2,Jia-feng YANG1,Lu-rong JIANG1,*()
1. School of Information Sciences and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China
2. Department of Neurosurgery, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310003, China
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Abstract  

A resistive film pressure sensor was embedded in the mattress, and the variational modal decomposition (VMD) algorithm was introduced into the two-dimensional physiological signal extraction process in order to monitor the ballistocardiogram (BCG) and respiratory signal in a non-invasive manner during sleep. The VMD algorithm was used to decompose the potential components of the BCG signal and respiratory signal in the physiological signal after the signal passes through the flexible pressure sensor in the mattress, hardware low-pass filtering, and digital detrending (DFA). The effective components were adaptively selected to reconstruct the BCG signal and the respiratory signal. The instantaneous frequencies of VMD, empirical mode decomposition (EMD), and complementary set empirical mode decomposition (CEEMD) components were compared based on Hilbert transform. The aliasing situation of VMD in 0~3.0 Hz was improved compared with EMD and CEEMD. The Bland-Altman method was used to evaluate the consistency of the standard results and experimental reconstruction results. Results show that 93.75% and 92.5% of the BCG and respiratory signals obtained by the VMD method are within the standard limit of 95% consistency, and there is a high consistency.

Key words： ballistocardiogram (BCG)      variational mode decomposition (VMD)      flexible pressure sensor      non-invasive manner      Bland-Altman     
Received: 17 September 2019      Published: 28 October 2020
CLC:  R 318  
Corresponding Authors: Lu-rong JIANG     E-mail: jijuntong@zstu.edu.cn;jianglurong@zstu.edu.cn
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Ji-jun TONG
Yan-jie BAI
Jian-wei PAN
Jia-feng YANG
Lu-rong JIANG
Cite this article:

Ji-jun TONG,Yan-jie BAI,Jian-wei PAN,Jia-feng YANG,Lu-rong JIANG. Ballistocardiogram and respiratory signal separation based on variational mode decomposition. Journal of ZheJiang University (Engineering Science), 2020, 54(10): 2058-2066.

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http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2020.10.024     OR     http://www.zjujournals.com/eng/Y2020/V54/I10/2058


基于变分模态分解的心冲击信号和呼吸信号分离

为了在睡眠时以非侵入方式监测心冲击信号（BCG）和呼吸信号，使用电阻式薄膜压力传感器嵌入床垫中，将变分模态分解（VMD）算法引入到二维生理信号提取过程. 信号经床垫中的柔性压力传感器，通过硬件低通滤波、数字去趋势（DFA）后，利用VMD算法分解出生理信号中心冲击信号与呼吸信号的潜在分量，通过自适应选取有效分量重构BCG信号与呼吸信号. 基于Hilbert变换，对比VMD、经验模态分解（EMD）、互补集合经验模态分解（CEEMD）分量的瞬时频率. VMD在0~3.0 Hz内的混叠情况相对于EMD与CEEMD得到改善. 采用Bland-Altman法，对标准结果和实验重构结果进行一致性评价. 结果表明，利用VMD法所得BCG与呼吸信号分别有93.75%和92.5%的点在95%一致性标准界限内，有较高的一致性.


关键词： 心冲击信号（BCG）,  变分模态分解（VMD）,  柔性压力传感器,  非入侵方式,  Bland-Altman 
Fig.1 Typical BCG signal
Fig.2 Flow chart of physiological signal decomposition and reconstruction
Fig.3 Physiological signal acquisition device
Fig.4 Comparison of original signal before and after detrending
Fig.5 
Fig.5 Comparison of decomposition results in different algorithms
Fig.6 Comparison of Hilbert-Huang spectra after decomposition in different algorithms
分量 Pb
1 2 3 4 5 6 7 8 9 10
EMD呼吸能量 0.000 8 0.010 2 0.011 0 0.044 5 0.128 3 0.820 7 0.576 9 0.132 6 0.002 2 0.001 2
EMD心冲击能量 0.014 5 0.140 1 0.415 8 0.577 7 0.280 7 0.046 5 0.112 3 0.136 9 0.003 6 0.002 1
CEEMD呼吸能量 0.001 4 0.002 7 0.011 6 0.085 0 0.901 7 0.621 5 0.119 0 0.001 8 0.001 6 0.000 9
CEEMD心冲击能量 0.014 2 0.212 4 0.442 4 0.388 2 0.171 6 0.168 5 0.130 0 0.009 0 0.000 5 0.000 4
VMD呼吸能量 0.858 1 0.251 7 0.004 8 0.002 3 0.000 6 0.000 3 0.000 2 0.000 2 0.000 1 0.000 1
VMD心冲击能量 0.105 3 0.111 6 0.394 0 0.318 9 0.303 7 0.068 5 0.021 2 0.008 1 0.003 9 0.001 1
Tab.1 Analysis of physiological signal energy ratio
Fig.7 Analysis of physiological signal energy ratio
Fig.8 Comparison of reconstruction effect in different decomposition algorithms
Fig.9 Consistency analysis based on Bland-Altman method
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