大鼠心血管信号处理及其在模拟微重力实验中的应用
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摘要
当在失重(微重力)环境停留一段时间后，由于一系列适应性改变，航天员一般并无明显心血管症状；但当返回地面1G重力环境时，却普遍出现明显的心血管失调症状，不但直接威胁航天员返回、着陆时的安全，也影响其对地面重力环境的再适应能力。目前，改善心血管失调的对抗措施分为两类：即以运动为主的对抗措施和以重力为基础的对抗措施；基于重力的对抗措施又可细分为连续性人工重力对抗(continuous artificial gravity，CAG)和间断性人工重力(intermittent artificial gravity，IAG)对抗两类。研究表明通过在太空船安装短臂离心机(short arm centrifuge，SAC)，以此实现的IAG可有效替代CAG的对抗作用。然而，在微重力环境下进行人体实验受多方条件制约且费用昂贵，同时在地面进行卧床研究也受到方法学限制，而采用动物模型实验，可以进行多层次、多学科实验研究，补充人体实验之不足。
在心血管疾病及微重力心血管生理研究中，大鼠是最重要的实验动物之一。利用大鼠模型，可以进行从细胞、组织、器官到整体多个层次的观察，但对其心率变异性(heart rate variability，HRV)与血压变异性
The cardiovascular function of cosmonauts is generally well maintained in space due to adaptational changes. Whereas on returning to 1-G environment of Earth, most of them experience symptoms of orthostatic intolerance lasting from several hours to even several days, depending on duration of microgravity exposure. Besides, upright exercise capacity is also commonly reduced postflight, which is also related to orthostatic intolerance and cardiovascular deconditioning due to microgravity exposure. Marked presyncopal symptoms and frank syncope could seriously or completely impair the crew member's performance during entry, landing, and egress after long-duration spaceflights. However, currently used exercise-based countermeasures have limited success. For future long-duration missions, intermittent artificial gravity (IAG) by incorporating a short arm centrifuge (SAC) into the spacecraft has been suggested as an alternate to continuous artificial gravity (CAG). Before a short-arm centrifuge can in space be tested, the effective countermeasure must be examined. However, there are many difficulties and limitations in human studies. Methodological difficulties are
在心血管疾病及微重力心血管生理研究中，大鼠是最重要的实验动物之一。利用大鼠模型，可以进行从细胞、组织、器官到整体多个层次的观察，但对其心率变异性(heart rate variability，HRV)与血压变异性
The cardiovascular function of cosmonauts is generally well maintained in space due to adaptational changes. Whereas on returning to 1-G environment of Earth, most of them experience symptoms of orthostatic intolerance lasting from several hours to even several days, depending on duration of microgravity exposure. Besides, upright exercise capacity is also commonly reduced postflight, which is also related to orthostatic intolerance and cardiovascular deconditioning due to microgravity exposure. Marked presyncopal symptoms and frank syncope could seriously or completely impair the crew member's performance during entry, landing, and egress after long-duration spaceflights. However, currently used exercise-based countermeasures have limited success. For future long-duration missions, intermittent artificial gravity (IAG) by incorporating a short arm centrifuge (SAC) into the spacecraft has been suggested as an alternate to continuous artificial gravity (CAG). Before a short-arm centrifuge can in space be tested, the effective countermeasure must be examined. However, there are many difficulties and limitations in human studies. Methodological difficulties are
引文
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Zhang LF, Yu ZB, Ma J, and Mao QW. Peripheral effector mechanism hypothesis of postflight cardiovascular dysfunction. Aviat Space Environ Meal 72:567-575, 2001;
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Zhang LE Heart rate and blood pressure variability: analytical methods, physiological significance and its applications [J]. Progress in Physiological Sciences, 1996,27(4):295-300.
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[39] 钟季康，宋志怀，郝为强．RQA在肌电分析中的应用[J]．生物物理学报，2002，18(2)：241-245．
Zhong JK, Song ZH, Hao WQ. Application of recurrence qualification analysis to EMG [J]. Acta Biophysica Sinica, 2002,18(2): 241-245.
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[2] 张立藩，等．航天飞行后心血管失调的外周效应器机制假说．生理科学进展32：13-17，2001．
Zhang LF, Yu ZB, Ma J, and Mao QW. Peripheral effector mechanism hypothesis of postflight cardiovascular dysfunction. Aviat Space Environ Meal 72:567-575, 2001;
[3] Blomqvist CG. Regulation of the systemic circulation at microgravity and during readaptaion to 1G. Med Sci Sports Exerc 28: S9-S13, 1996.
[4] Tipton CM. Animal models and their importance to human physiological responses in microgravity. Med Sci Sprots Exert 28: S94-S100, 1996.
[5] Yamasaki M, et al. Spaceflight alters the fiber composition of the aortic nerve in the developing rat. Neuroscience 128:819-829,2004.
[6] Musacchia XJ, Fagette S. Weightlessness simulations for cardiovascular and muscle systems: validity of rat models. J Gravitational Physiol 4: 49-60,1997.
[7] Morey-Holton ER and Globus RK. Invited Review: Hindlimb unloading rodent model: technical aspects. J Appl Physiol 92:1367-1377 2002.
[8] Sun B, Zhang LF, et al. Daily short-period gravitation can prevent functional and structural changes in arteries of simulated microgravity rats. J Appl Physiol 97:1022-1031, 2004.
[9] Persson PB. Modulation of cardiovascular control mechanisms and their interaction. Physiol Rev 76: 193-224, 1996.
[10] Kuo TBJ, Yang CCH. Altered frequency characteristic of central vasomotor control in SHR. Am J Physiol 278: H201-H207,2000.[11] Su CJ, Bao JX, Zhang LF, Rao ZR. Fos protein expression in the medulla oblongata and changes in size of spinal lateral horn neurons after 4-wk simulated weightlessness in rats. J Gravitational Physiol 7: 71-78, 2000.
[12] Zhang LF. Invited Review: Vascular adaptation to microgravity: what have we learned? J Appl physiol. 91: 2415-2430, 2001.
[13] 张立藩．心率与血压的变异性：分析方法、生理意义及其应用[J]．生理科学进展，1996，27(4)：295-300．
Zhang LE Heart rate and blood pressure variability: analytical methods, physiological significance and its applications [J]. Progress in Physiological Sciences, 1996,27(4):295-300.
[14] 张立藩，王守岩，牛有国．心率与血压变异性的多变量、多维信号分析进展[J]．航天医学与医学工程，2002，13(3)：157-162．
Zhang LF, Wang SY, Niu YG. Recent advances in multi-variate and multi-dimensional analysis of heart rate variability and blood pressure variability [J]. Space Medicine & Medical Engineering, 2002, 15 (3) :157-162.
[15] Dabire H, Mestivier D, Jarnet J, et al. Quantification of sympathetic and parasympathetic tones by nonlinear indexes in normotensive rats [J]. Am J Physiol, 1998, 275 (Heart Circ Physiol 44): 1290-1297
[16] Mansier P, Clairambault J, Charlotte N, et al. Linear and non-linear analyses of heart rate variability : a minireview[J]. Cardiovascular Research, 1996,31 (3) : 371-379.
[17] Guyton AC,Coleman TG, Cowley AW Jr, Scheel KW, Manning RD, Norman RA Jr. Arterial pressure regulation: overriding dominance of the kidneys in long-term regulation and in hypertension. Am J Med. 1972; 52:584-594
[18] Irigoyen MC, Moreira ED, Ida F, Pires M, Cestari IA, Krieger EM. Changes of renal sympathetic activity in acute and chronic conscious sinoaortic denervated rats. Hypertension. 1995;26:1-6[19]Mancia G, Zanchetti A. Blood pressure variability. In: Zanchetti A, Tarazi RC. Handbook of Hypertension: Pathophysiology of Hypertension: Cardiovascular Aspects. Amsterdam, Netherlands: Elsevier; 1986;7:125-152
[20]Wagner CD, Nafz B, Persson PB. Chaos in blood pressure control. Cardiovasc Res. 1996;31:380-387
[21]Kantz H and Schreiber T. Human ECG: nonlinear deterministic versus stochastic aspects. IEEE Proc Sci Measurem Technol 145:279-284, 1998.
[22]Patapov A. Are R-R intervals data appropriate to study the dynamic of heart? In: Nonlinear Analysis of Physiological Data, edited by Kantz H, Kurths J, and Mayer-Kress G. Berlin: Springer-Verlag, 1998,p.117-128
[23]Braun C, kowallik P, Freking A, Hadeler D, Kniffki KD, and Meesmann M. Demonstration of nonlinear components in heart rate variability of healthy persons. Am J Physiol Heart Circ Physiol 275:H1577-H1584,1998
[24]TH. Stefenlli et al., Heart rate behaviour at different stage of congestive heart failure, European Heart J. vol 13, 1992,P902-907
[25]Poon CS. Decrease of cardiac chaos in congestive heart failure. Nature 389:492-495,1997
[26]Wagner CD, Stauss HM, Persson PB, and Kregel KC. Correlation integral of blood pressure as a maker for exercise intensities. Am J Physiol Regulatory Integrative Comp Physiol275:R1661-R1666,1998
[27]Hoyer D, Bauer R, Walter B, and Zwiener U. Estimation of non-linear couplings on the basis of complexity an predictability-a new method applied to cardiorespiratory coordination. IEEE Trans Biomed Eng 45:545-552,1998
[28]Alomg, Y., S.Eliash, O.Oz, and S.Akserod. Nonlinear analysis of BP signal. Can it detect malfunctions in BP control? Am.J.Physiol. 271 (Heart Circ.Physiol.40):H396-403,1996
[29] Mestivier, D., N.P.Chau, X.Chanudet, B.Bauduceau, and P.Larroque. Relationship between diabetic autonomic dysfunction and heart rate variability assessed by recurrence plot.Am.J.Physiol. 272(Heart Circ. Physiol. 41):H1094-H1099,1997
[30] Zbilut, J.P., C.L.Webber, Jr., and M.Zak. Quantification of heart rate variability using methods derived from nonlinear dynamic. In: Assessrnent and Analysis of Cardiovascular Function, edited by G.Drzewiecki and J.K.J.Li New York:Springer 324-334, 1998
[31] Webber, C.L., and J.P.Zbilut. Dynamical assessment of physiological systems and states using recurrence plot strategies. J.Appl.Physiol. 76:965-973,1994
[32] Eckmann, J.P., S.O.Kamphorst, and D.Rueele. Recurrence plot of dynamical systems. Europhys.Lett. 4: 973-977, 1987
[33] Kennel, M.B., R.Brown, and H.D.I.Abarbanel. Determining embedding dimension for a phase-space reconstruction using a geometrical construction. Phys.Rev.A 45:3403-3411,1992
[34] Wolf, A., J.B.Swift, H.Swinney, and J.A.Vastano. determining Lyapunov exponents from a times series. Physica D 16:285-317,1985
[35] Morey-Holton ER, Globus RK. Hindlimb unloading rodent model: technical aspects [J]. J Appl Physiol. 2002, 92(4): 1367-1377.
[36] Sun B, Cao XS, Zhang LF, et al. Daily 4-h head-up tilt is effective in preventing muscle but not bone atrophy due to simulated microgravity [J]. J Gray Physiol, 2003,10(2):29-38.
[37] Mestivier D, Dabire H, Safar M, et al. Use of nonlinear methods to assess effects of clonidine on blood pressure in spontaneously hypertensive rats [J]. J Appl Physiol, 1998, 84(5): 1795-1800.
[38] 夏恒超，詹永麒．基于近邻轨道平均长度的混沌时间序列分类方法[J]．物理学报，2004，53(5)：1299-1304．??Xia HC, Zhan YQ. Classification of chaotic time series data based on the average length of close orbits [J]. Acta Physica Sinica, 2004, 53(5): 1299-1304.
[39] 钟季康，宋志怀，郝为强．RQA在肌电分析中的应用[J]．生物物理学报，2002，18(2)：241-245．
Zhong JK, Song ZH, Hao WQ. Application of recurrence qualification analysis to EMG [J]. Acta Biophysica Sinica, 2002,18(2): 241-245.
[40] Fagette S, Lo M, Gharib C, et al. Cardiovascular variability and baroreceptor reflex sensitivity over a 14-day tail suspension in rats [J]. J Appl Physiol, 1995,78(2):717-724.
[41] Martel E, Ponchon P, Champeroux P, et al. Mechanisms of the cardiovascular deconditioning induced by tail suspension in the rat [J]. Am J Physiol, 1998, 274(Heart Circ Physiol 43): 1667-1673.
[42] Paciard N H, Grutchfield J P, Farmer J D, et al. Geometry from a time series (J), Phys Rev Lett, 1980, vol. 45, No. 9, 712—716.
[43 Takens F, Detecting strange attractors in turbulence(J), Lecture Notes in Mathematics, Dynamical Systems and Turbulence, 1981, vol. 898, pp.365-381.
[44] Hao Bailin, Elementary Symbolic Dynamics and Chaos in Dissipative Systems(M), World Scientific, New Jersey, 1989, pp. 196-201.
[45] Grassberger P Proeaccia, Measuring the strangeness of strange attractors川, Physica D, 1983, vol. 9, pp. 189-208.
[46] Kennel M B,Brown R and Abarbanel H D I, Determing embedding dimension for phase-space reconstruction using a geometrical construction (J), Phys. Rev. A, 1992, vol. 45, No. 6, pp. 3403-3411.
[47] Broomhead D S and King G P, Extracting qualitative dynamics from experimental data(J), Physiea D, 1986, vol. 20, pp. 217-336.[48]Fraser A M and Swinney H L, Independent coordinates for strange attractors from mutual information (J), Phys Rev A, 1986, vol. 33, No. 2,pp. 1134-1140.
[49]Farmer J D and Sidorowich J, Predicting chaotic time series(J), Phys.Rev. Lett, 1987, vol. 59, No. 8, pp. 845-848.
[50]J.-P Eckmann and D. Ruelle, Ergodic theory of chaos and strange attractors, Rev. Mod. Phys, 1985, vol. 57, pp. 617.
[51]H. Kantz, A robust method to estimate the maximal Lyapunov exponent of a time series, Phys. Lett. A, 1994, vol. 185, pp.77-87.
[52]M. Sano and Y Sawada, Measurement of the Lyapunov spectrum from a chaotic time series, 1985, Phys. Rev. Lett, vol. SS, pp. 1082.
[53]Wolf A,Swift J B,Swinney H L, et al, Determing Lyapunov exponents from a time series(J), Physica D, 1985, vol. 16, pp. 285-317.
[54]S. M. Pincus, Approximate entropy as a measure of system complexity, Proc. Natl. Acad. Sci. USA, 1991, vol. 88, pp. 2297-2301.
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