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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2020, Vol. 54 Issue (2): 340-347    DOI: 10.3785/j.issn.1008-973X.2020.02.015
Computer Technology, Information Engineering     
Human hemorheology information evaluation based on Hilbert-Huang transform to decompose photoplethysmography signal
Lu YU(),Long-zhe JIN*(),Ming-wei XU,Jian-guo LIU
School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China
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Abstract  

A hypoxia experiment was designed, in order to identify the dynamic component of the hemorheology information in the photoplethysmography (PPG) signal and analyze its characteristics. A total of thirty subjects were measured for PPG signals under normal oxygen volume fraction condition (20%~21%) and low oxygen volume fraction condition (15%~16%), respectively. The signal was analyzed based on the Hilbert-Huang transform (HHT) algorithm. The empirical mode decomposition results show that the dynamic component actually representing the hemorheology information of the PPG signal is intrinsic mode function IMFX. There are two time domain features of IMFX, one is a waveform similar to the arterial systolic relaxation, and the other is a periodic oscillation. The instantaneous frequency and marginal spectrum of IMFX were obtained based on the Hilbert transform algorithm, and the instantaneous frequency was mostly 1.5~2.5 Hz. In the hypoxic environment, the amplitude of the Hilbert marginal spectrum in the above frequency range is significantly smaller than that of the normal oxygen environment (P<0.05), which proves that this feature can be used to determine the hemorheological changes caused by hypoxia.

Key words： photoplethysmography (PPG)      Hilbert-Huang transform (HHT)      intrinsic mode function      empirical mode decomposition      hemorheology      hypoxic environment     
Received: 22 May 2019      Published: 10 March 2020
CLC:  X 914  
Corresponding Authors: Long-zhe JIN     E-mail: yulubaobeihao@163.com;lzjin@ustb.edu.cn
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Lu YU
Long-zhe JIN
Ming-wei XU
Jian-guo LIU
Cite this article:

Lu YU,Long-zhe JIN,Ming-wei XU,Jian-guo LIU. Human hemorheology information evaluation based on Hilbert-Huang transform to decompose photoplethysmography signal. Journal of ZheJiang University (Engineering Science), 2020, 54(2): 340-347.

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http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2020.02.015     OR     http://www.zjujournals.com/eng/Y2020/V54/I2/340


基于HHT分解光电容积脉搏波信号的人体血液流变信息评估

为了识别人体光电容积脉搏波（PPG）信号中表征血液流变信息的动力分量并分析其特点，设计低氧实验. 测量30位受试者在正常氧（20%~21%）和极端低氧（15%~16%）2种氧气体积分数环境中的PPG信号，利用希尔伯特黄变换（HHT）算法分解信号. 通过经验模式分解得到，PPG信号中实际表征血液流变信息的动力分量为固有模式函数IMFX，其时域特点有2个，一个是有类似动脉收缩舒张的波形，另一个是周期性振荡. 基于Hilbert变换得到IMFX的瞬时频率和边际谱，其瞬时频率大多为1.5~2.5 Hz，且在低氧环境中此频率段内的边际谱幅值显著小于正常氧环境情况下的（P<0.05），证明利用该特征可以有效识别低氧诱导的血液流变变化.


关键词： 光电容积描记术（PPG）,  希尔伯特黄变换（HHT）,  固有模态函数,  经验模式分解,  血液流变学,  低氧环境 
Fig.1 Hemorheology information test experiment under hypoxia environment
Fig.2 Photoplethysmography signal acquisition device
环境 性别 心率/（次?min?1）
1）注：数值为平均值±标准差
正常氧环境 男性 70±2.31)
女性 83±2.1
低氧环境 男性 102±3.6
女性 119±1.8
Tab.1 Subject heart rates in normal oxygen environment and hypoxic environment
Fig.3 Variation of oxygen volume fraction in refuge
Fig.4 PPG waveform after pre-processing
Fig.5 Empirical mode decomposition algorithm architecture
Fig.6 Empirical mode decomposition results of photoplethysmography signal
Fig.7 Statistical analysis of intrinsic mode function
Fig.8 Hilbert marginal spectrum of IMF
Fig.9 Schematic diagram of IMFX marginal spectrum theory process
Fig.10 Statistical results of area ratio
编号 φ 全瞬时频率边际谱
振幅/(μV2·Hz?1) 		P 1.5~2.5 Hz边际谱
振幅/(μV2·Hz?1) 		P 编号 φ 全瞬时频率边际谱
振幅/(μV2·Hz?1) 		P 1.5~2.5 Hz边际谱
振幅/(μV2·Hz?1) 		P
1）注：边际谱振幅为平均值±标准差，独立样本t检验
1 低 2.4±35.61） P<0.05 461.1±270.1 P<0.05 16 低 3.9±45.0 P<0.05 390.3±106.0 P<0.05
正 8.3±95.4 1 336.2±405.0 正 4.4±51.0 538.1±537.8
2 低 2.4±24.6 P<0.05 336.5±101.1 P<0.05 17 低 4.9±58.2 P<0.05 722.5±119.7 P<0.05
正 3.9±46.8 545.4±285.9 正 7.8±83.7 955.8±437.8
3 低 2.4±35.4 P<0.05 249.9±250.8 P<0.05 18 低 3.7±46.8 P<0.05 757.7±201.3 P<0.05
正 8.8±91.0 809.4±552.5 正 8.2±79.4 1 063.3±439.0
4 低 4.8±58.1 P<0.05 804.8±301.7 P<0.05 19 低 3.6±34.6 P>0.05 480.5±298.9 P<0.05
正 8.2±93.8 1 156.3±431.3 正 4.8±58.1 656.0±212.2
5 低 3.4±35.3 P>0.05 398.6±240.1 P<0.05 20 低 4.4±45.7 P>0.05 424.0±241.4 P<0.05
正 3.8±55.6 664.7±438.9 正 5.2±95.9 1 020.4±94.3
6 低 7.0±77.2 P>0.05 956.3±372.6 P<0.05 21 低 6.8±66.3 P>0.05 689.4±299.5 P<0.05
正 7.8±100.2 1 430.0±456.7 正 7.5±88.2 1 448.5±98.3
7 低 3.1±39.7 P>0.05 273.8±290.4 P<0.05 22 低 6.3±78.2 P>0.05 947.4±371.8 P<0.05
正 4.3±53.0 443.5±190.7 正 6.9±46.7 1 212.6±200.8
8 低 1.8±23.0 P>0.05 238.0±137.2 P<0.05 23 低 3.1±70.8 P>0.05 653.8±292.4 P<0.05
正 2.6±30.8 415.7±158.5 正 5.7±27.6 920.5±382.2
9 低 2.4±28.1 P<0.05 362.0±77.3 P<0.05 24 低 2.8±50.7 P>0.05 340.5±211.0 P<0.05
正 9.8±128.6 1 378.7±1 205.5 正 3.7±42.1 666.4±622.8
10 低 2.6±35.6 P<0.05 456.1±269.1 P<0.05 25 低 3.6±27.4 P>0.05 346.5±147.8 P<0.05
正 8.6±95.4 1 336.2±405.0 正 5.6±79.6 679.8±902.8
11 低 2.4±25.8 P<0.05 466.7±360.5 P<0.05 26 低 2.8±18.2 P>0.05 667.9±322.2 P<0.05
正 8.6±56.7 828.2±402.5 正 4.8±56.7 1 236.7±980.6
12 低 3.2±38.6 P>0.05 268.7±180.2 P<0.05 27 低 4.7±52.8 P<0.05 713.4±116.9 P<0.05
正 4.3±52.8 420.6±180.5 正 7.6±78.2 985.6±420.8
13 低 2.3±35.9 P<0.05 385.6±270.3 P<0.05 28 低 3.8±34.2 P<0.05 460.4±278.3 P<0.05
正 8.6±94.7 678.2±540.7 正 4.7±52.1 1 042.7±580.5
14 低 7.1±67.3 P>0.05 964.3±342.6 P<0.05 29 低 3.5±34.2 P>0.05 462.0±278.7 P<0.05
正 7.6±102.4 1 328.7±568.9 正 4.7±57.1 667.0±548.3
15 低 2.4±25.7 P<0.05 248.0±168.2 P<0.05 30 低 2.4±28.7 P<0.05 362.0±77.3 P<0.05
正 4.5±55.7 737.6±306.5 正 9.9±100.6 1 378.7±1 202.5
Tab.2 IMFX marginal spectrum amplitude statistics for all subjects
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