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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 (2): 340-347    DOI: 10.3785/j.issn.1008-973X.2020.02.015
计算机技术、信息工程     
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
于露(),金龙哲*(),徐明伟,刘建国
北京科技大学 土木与资源工程学院，北京 100083
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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摘要：

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    
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
收稿日期: 2019-05-22 出版日期: 2020-03-10
CLC:  X 914  
基金资助: 国家“十三五”重点研发计划资助项目（2016YFC0801700）
通讯作者: 金龙哲     E-mail: yulubaobeihao@163.com;lzjin@ustb.edu.cn
作者简介: 于露（1990—），女，博士生，从事有限空间、光电容积脉搏波信号研究. orcid.org/0000-0002-0306-024X. E-mail： yulubaobeihao@163.com
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引用本文:

于露,金龙哲,徐明伟,刘建国. 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[J]. 浙江大学学报(工学版), 2020, 54(2): 340-347.

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.

链接本文:

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

图 1  低氧条件下的人体血液流变测试实验
图 2  光电容积脉搏波信号采集装置
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
表 1  正常氧与低氧条件下受试者心率
图 3  救生舱中氧气体积分数变化
图 4  预处理后的PPG波形图
图 5  经验模式分解算法架构
图 6  光电容积脉搏波信号的经验模式分解结果
图 7  固有模态函数的统计学分析
图 8  IMF的Hilbert边际谱图
图 9  IMFX边际谱理论处理过程示意图
图 10  面积比值的统计结果
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
表 2  所有受试者IMFX边际谱振幅统计表
1 ALLEN J Photoplethysmography and its application in clinical physiological measurement[J]. Physiological Measurement, 2007, 28 (3): 1- 39
doi: 10.1088/0967-3334/28/3/R01
2 REISNER A, SHALTIS P A, MCCOMBIE D, et al Utility of the photoplethysmogram in circulatory monitoring[J]. Anesthesiology, 2008, 108 (5): 950- 958
doi: 10.1097/ALN.0b013e31816c89e1
3 KAMAL A A R, HARNESS J B, IRVING G, et al Skin photoplethysmography: a review[J]. Computer Methods and Programs in Biomedicine, 1989, 28 (4): 257- 269
doi: 10.1016/0169-2607(89)90159-4
4 KAMSHILIN A A, NIPPOLAINEN E, SIDOROV I S, et al A new look at the essence of the imaging photoplethysmography[J]. Scientific Reports, 2014, 5 (5): 10494
5 VOLKOV M V, MARGARYANTS N B, POTEMKIN A V, et al Video capillaroscopy clarifies mechanism of the photoplethysmographic waveform appearance[J]. Scientific Reports, 2017, 7 (1): 13298
doi: 10.1038/s41598-017-13552-4
6 NITZAN M, ADAR Y, HOFFMAN E, et al Comparison of systolic blood pressure values obtained by photoplethysmography and by korotkoff sounds[J]. Sensors, 2013, 13 (11): 14797- 14812
doi: 10.3390/s131114797
7 TAMURA T, MAEDA Y, SEKINE M, et al Wearable photoplethysmographic sensors: past and present[J]. Electronics, 2014, 3 (2): 282- 302
doi: 10.3390/electronics3020282
8 DALY S M, LEAHY M J ‘Go with the flow’: a review of methods and advancements in blood flow imaging[J]. Journal of Biophotonics, 2013, 6 (3): 217- 255
doi: 10.1002/jbio.201200071
9 WOWERN E V, ?STLING G, NILSSON P M, et al Digital photoplethysmography for assessment of arterial stiffness: repeatability and comparison with applanation tonometry[J]. Plos One, 2015, 10 (8): e0135659
doi: 10.1371/journal.pone.0135659
10 NJOUM H, KYRIACOU P A Photoplethysmography for the assessment of haemorheology[J]. Scientific Reports, 2017, 7 (1): 1406
doi: 10.1038/s41598-017-01636-0
11 LEE C, SIK S H, LEE M Relations between ac-dc components and optical path length in photoplethysmography[J]. Journal of Biomedical Optics, 2011, 16 (7): 077012
doi: 10.1117/1.3600769
12 KIM J M, CHOI J K, CHOI M, et al Assessment of cerebral autoregulation using continuous-wave near-infrared spectroscopy during squat-stand maneuvers in subjects with symptoms of orthostatic intolerance[J]. Scientific Reports, 2018, 8 (1): 13257
doi: 10.1038/s41598-018-31685-y
13 XING X, SUN M Optical blood pressure estimation with photoplethysmography and FFT-based neural networks[J]. Biomedical Optics Express, 2016, 8 (7): 3007- 3020
14 RAM M R, MADHAV K V, KRISHNA E H, et al A novel approach for motion artifact reduction in PPG signals based on AS-LMS adaptive filter[J]. IEEE Transactions on Instrumentation and Measurement, 2012, 61 (5): 1445- 1457
doi: 10.1109/TIM.2011.2175832
15 WANG C, ZHANG J L, XUN B W Monitoring heart and respiratory rates at radial artery based on PPG[J]. Optik, 2013, 19 (124): 3954- 3956
16 ELGENDI M Optimal signal quality index for photoplethysmogram signals[J]. Bioengineering, 2016, 4 (3): 21
17 于露, 金龙哲, 徐明伟, 等 基于光电容积脉搏波的有限空间生理疲劳测量[J]. 工程科学学报, 2018, 40 (10): 1215- 1222
YU Lu, JIN Long-zhe, XU Ming-wei, et al Confined space physiological fatigue measurement based on photoplethysmographypulse wave signal[J]. Chinese Journal of Engineering, 2018, 40 (10): 1215- 1222
18 HUANG N E, WU Z A review on Hilbert‐Huang transform: method and its applications to geophysical studies[J]. Reviews of Geophysics, 2008, 46 (2): 1- 23
19 PENG Z K, PETER W T, CHU F L A comparison study of improved Hilbert-Huang transform and wavelet transform: application to fault diagnosis for rolling bearing[J]. Mechanical Systems and Signal Processing, 2005, 19 (5): 974- 988
doi: 10.1016/j.ymssp.2004.01.006
20 YAN R Q, GAO R X Hilbert-Huang transform-based vibration signal analysis for machine health monitoring[J]. IEEE Transactions on Instrumentation and Measurement, 2006, 55 (6): 2320- 2329
doi: 10.1109/TIM.2006.887042
21 WU Z H, HUANG N E Ensemble empirical mode decomposition: a noise-assisted data analysis method[J]. Advances in Adaptive Data Analysis, 2009, 1 (01): 1- 41
doi: 10.1142/S1793536909000047
22 AYACHE S S, AL-ANI T, LEFAUCHEUR J P Distinction between essential and physiological tremor using Hilbert-Huang transform[J]. Neurophysiologie Clinique/Clinical Neurophysiology, 2014, 44 (2): 203- 212
23 TYAN C C, LIU S H, CHEN J Y, et al A novel noninvasive measurement technique for analyzing the pressure pulse waveform of the radial artery[J]. IEEE Transactions on Bio-medical Engineering, 2008, 55 (1): 288- 297
doi: 10.1109/TBME.2007.910681
24 WU H T, LEE C H, LIU A B, et al Arterial stiffness using radial arterial waveforms measured at the wrist as an indicator of diabetic control in the elderly[J]. IEEE Transactions on Biomedical Engineering, 2011, 58 (2): 243- 252
doi: 10.1109/TBME.2010.2084087
25 WEI H C, XIAO M X, CHEN H Y, et al Instantaneous frequency from Hilbert-Huang transformation of digital volume pulse as indicator of diabetes and arterial stiffness in upper-middle-aged subjects[J]. Scientific Reports, 2018, 8 (1): 15771
doi: 10.1038/s41598-018-34091-6
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