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浙江大学学报(工学版)
浙江大学学报(工学版)  2020, Vol. 54 Issue (10): 2058-2066    DOI: 10.3785/j.issn.1008-973X.2020.10.024
生物医学工程     
基于变分模态分解的心冲击信号和呼吸信号分离
童基均1(),柏雁捷1,潘剑威2,杨佳锋1,蒋路茸1,*()
1. 浙江理工大学 信息学院，浙江 杭州 310018
2. 浙江大学医学院附属第一医院 神经外科，浙江 杭州 310003
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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摘要：

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    
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
收稿日期: 2019-09-17 出版日期: 2020-10-28
CLC:  R 318  
基金资助: 浙江省重点研发计划资助项目（2015C03023）；浙江理工大学基本科研业务费资助项目（2019Q042）；浙江理工大学“521人才培养计划”资助项目
通讯作者: 蒋路茸     E-mail: jijuntong@zstu.edu.cn;jianglurong@zstu.edu.cn
作者简介: 童基均（1977—），男，教授，从事传感器及检测技术、计算机视觉的研究. orcid.org/0000-0002-6209-6605. E-mail： jijuntong@zstu.edu.cn
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童基均
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引用本文:

童基均,柏雁捷,潘剑威,杨佳锋,蒋路茸. 基于变分模态分解的心冲击信号和呼吸信号分离[J]. 浙江大学学报(工学版), 2020, 54(10): 2058-2066.

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.

链接本文:

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

图 1  心冲击信号典型波形
图 2  生理信号分解重构流程图
图 3  生理信号采集装置
图 4  原始信号去趋势前、后的对比
图 5  
图 5  不同算法的分解结果对比
图 6  利用不同算法分解后的Hilbert-Huang谱对比
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
表 1  生理信号能量占比分析
图 7  生理信号能量占比分析
图 8  不同分解方法下重构效果对比
图 9  基于Bland-Altman法的一致性分析
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