直立倾斜引起的心率和血压的耦合性变化分析
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摘要
从动态和稳态两个视角,研究直立倾斜(HUT)引起体位改变前后以及不同速度改变体位过程中RR间期(RRI)与收缩压(SBP)间耦合性的变化。所用数据来自Phsio Net发布的体位变化所引起的生理响应数据库(PRCP),含有10位健康受试者(5男5女)在HUT过程中记录的连续心电和动脉血压信号。慢速体位变化(ST)和快速体位(RT)变化分别为在50和2 s之间从水平仰卧升至75°倾斜。提取逐拍RRI和SBP数据后,运用交叉时频分析和信息分解方法,结合时域和短时分形指数(α1),进行RRI和SBP时间序列的联合分析。信息分解分析结果表明,所有的显著差异集中在压力反射导致心率变化的后向反馈回路(SBP→RRI),ST后心率的可预测性较平卧时显著增高(0.416±0.067 vs 0.626±0.127),压力反射支路的SBP-RRI耦合性升高。而在RRI→SBP方向,HUT对其几乎没有影响。ST和RT之前,所有的同类指标相比均无显著差异。ST和RT之后的稳态,虽然RRI无显著差异,但较之ST之后,RT之后RRI的变异系数显著升高(0.054±0.014 vs 0.074±0.027),α1显著降低(1.45±0.25vs 1.28±0.27)。同时,交叉时频分析结果揭示了ST和RT过程中自主神经不同的动态反应行为。研究证明了信息分解方法的有效性,可明确区分心率与血压相互作用时的前向反馈和后向反馈的主导因果方向,而且可反映HUT前后信号可预测性的变化。
This study is aimed to investigate the changes of the coupling strength between RR interval( RRI)and systolic blood pressure( SBP) before and after head-up tilt( HUT) with different tilt speeds from dynamic and steady perspectives. The data used was from database Physiologic Response to Changes in Posture( PRCP)published on Physio Net,providing documentary ECG and continuous arterial blood pressure signals of ten healthy subjects( 5 males and 5 females) during HUT stimulation. Beat-by-beat time series of RRI and SBP were extracted from both slow tilt( ST,75°HUT over 50 s) and rapid tilt( RT,75°HUT over 2 s). Then,timefrequency analysis and information decomposition analysis,combined with time-domain indexes and short-term fractal exponent( α1) were applied to perform joint analysis between RRI and SBP. The results of information decomposition analysis indicated that all of the significant differences appeared in the feedback direction( SBP→RRI) due to baroreflex control on RRI. The prediction of RRI after ST significantly increased compared to that in supine position( 0. 416±0. 067 vs 0. 626±0. 127),indicating the elevation of the coupling strength along the baroreflex. However,HUT showed few effects in the feedforward direction of RRI → SBP. There were nosignificant differences between ST and RT for all of the same indexes before HUT. However,the coefficient of variation of RRI( CVRRI) in the steady state after RT was significantly increased and α1was significantly decreased compared to that after ST despite the fact that there was no difference for RRI. What's more,the results of time-frequency analysis suggested the different behavior of dynamic response to ST and RT. Our research proved the effectiveness of information decomposition analysis to detect the dominant causal direction( feedback or feedforward) in the RRI-SBP interactions and to characterize the changes of the prediction of RRI and SBP signal before and after HUT.
This study is aimed to investigate the changes of the coupling strength between RR interval( RRI)and systolic blood pressure( SBP) before and after head-up tilt( HUT) with different tilt speeds from dynamic and steady perspectives. The data used was from database Physiologic Response to Changes in Posture( PRCP)published on Physio Net,providing documentary ECG and continuous arterial blood pressure signals of ten healthy subjects( 5 males and 5 females) during HUT stimulation. Beat-by-beat time series of RRI and SBP were extracted from both slow tilt( ST,75°HUT over 50 s) and rapid tilt( RT,75°HUT over 2 s). Then,timefrequency analysis and information decomposition analysis,combined with time-domain indexes and short-term fractal exponent( α1) were applied to perform joint analysis between RRI and SBP. The results of information decomposition analysis indicated that all of the significant differences appeared in the feedback direction( SBP→RRI) due to baroreflex control on RRI. The prediction of RRI after ST significantly increased compared to that in supine position( 0. 416±0. 067 vs 0. 626±0. 127),indicating the elevation of the coupling strength along the baroreflex. However,HUT showed few effects in the feedforward direction of RRI → SBP. There were nosignificant differences between ST and RT for all of the same indexes before HUT. However,the coefficient of variation of RRI( CVRRI) in the steady state after RT was significantly increased and α1was significantly decreased compared to that after ST despite the fact that there was no difference for RRI. What's more,the results of time-frequency analysis suggested the different behavior of dynamic response to ST and RT. Our research proved the effectiveness of information decomposition analysis to detect the dominant causal direction( feedback or feedforward) in the RRI-SBP interactions and to characterize the changes of the prediction of RRI and SBP signal before and after HUT.
引文
[1]Faes L,Nollo G,Porta A.Mechanisms of causal interaction between short-term RR interval and systolic arterial pressure oscillations during orthostatic challenge[J].J Appl Physiol,2013,114:1657-1667.
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[11]Faes L,Nollo G,Chou KH.Assessment of Granger causality by nonlinear model identification application to short-term cardiovascular variability[J].Annals of Biomedical Engineering,2008,36:381-395.
[12]Granger CWJ.Investigating causal relations by econometric models and cross-spectral methods[J].Econometrica,1969,37:424-438.
[13]Faes L,Nollo G.Extended causal modeling to assess partial directed coherence in multiple time series with significant instantaneous interactions[J].Biological Cybernetics,2010,103:387-400.
[14]Widjaja D,Montalto A,Vlemincx E,et al.Cardiorespiratory information dynamics during mental arithmetic and sustained attention.PLo S ONE,2015,10(6):e0129112.
[15]Heldt T,Oefinger MB,Hoshiyama M,et al.Circulatory response to passive and active changes in posture[J].Computers in Cardiology,2003,30:263-266.
[16]孙中伟,彭屹.用于QT间期检测的复合算法研究[J].生物医学工程与临床,2009,13(3):184-188.
[17]许亮,王星,孙中伟,等.心率变异性时频分析参数用于心电图ST段偏移时段的判别[J].中国生物医学工程学报,2008,27(1):23-27.
[18]Peng CK,Havlin S,Stanley HE,et al.Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series[J].Chaos,1995,5(1):82-87.
[19]Tulppo MP,Kiviniemi AM,Hautala AJ,et al.Physiological background of the loss of fractal heart rate[J].Circulation,2005,112:314-319.
[20]Orini M,Bail'on R,Mainardi L,et al.Characterization of dynamic interactions between cardiovascular signals by timefrequency coherence[J].IEEE Transactions on Biomedical Engineering,2012,59(3):663-673.
[21]Visnovcova Z,Mestanik M,Javorka M,et al.Complexity and time asymmetry of heart rate variability are altered in acute mental stress[J].Physiol Meas,2014,35(7):1319-1334.
[22]Faes L,Porta A,Nollo G.Information decomposition in bivariate systems:theory and application to cardiorespiratory dynamics[J].Entropy,2015,17(1):277-303.
[23]Wehrwein EA,Joyner MJ.Regulation of blood pressure by the arterial baroreflex and autonomic Nervous System[M].//Buijs RM,Swaab DF.Autonomic Nervous System.Amsterdam:Elsevier,2013:89-102.
[2]Porta A,Catai AM,Takahashi ACM,et al.Causal relationships between heart period and systolic arterial pressure during graded head-up tilt[J].Am J Physiol Regul Integr Comp Physiol,2011,300:378-386.
[3]Furlan R,Piazza S,Dell’Orto S,et al.Cardiac autonomic patterns preceding occasional vasovagal reactions in healthy humans[J].Circulation,1998,98:1756-1761.
[4]Faes L,Nollo G.Bivariate nonlinear prediction to quantify the strength of complex dynamical interactions in short-term cardiovascular variability[J].Med Biol Eng Comput,2006,44:383-392.
[5]Faes L,Widesott L,Greco MD,et al.Causal cross-spectral analysis of heart rate and blood pressure variability for describing the impairment of the cardiovascular control in neutrally mediated Syncope[J].IEEE Transactions on Biomedical Engineering,2006,53(1):65-73.
[6]Assous S.Phase synchrony and coherence analysis of bio-signals using cross-time-frequency distribution[C]//Annual Conference of IEEE EMBS.Montreal:IEEE,2012:436-441.
[7]Orini M,Laguna P,Mainardi LT.Assessment of the dynamic interactions between heart rate and arterial pressure by the cross time-frequency analysis[J].Physiological Meas,2012,33:315-331.
[8]Faes L,Nollo G,Porta A.Compensated transfer entropy as a tool for reliably estimating information transfer in physiological time series[J].Entropy,2013,15:198-219.
[9]Porta A,Faes L,Nollo G.Conditional self-entropy and conditional joint transfer entropy in heart period variability during graded postural challenge[J].PLo S ONE,2015,10(7):e0132851(1-21).
[10]Faes L,Porta A,Nollo G.Mutual nonlinear prediction as a tool to evaluate coupling strength and directionality in bivariate time series:comparison among different strategies based on k-nearest neighbors[J].Phys Rev E,2008,78:026201(1-11).
[11]Faes L,Nollo G,Chou KH.Assessment of Granger causality by nonlinear model identification application to short-term cardiovascular variability[J].Annals of Biomedical Engineering,2008,36:381-395.
[12]Granger CWJ.Investigating causal relations by econometric models and cross-spectral methods[J].Econometrica,1969,37:424-438.
[13]Faes L,Nollo G.Extended causal modeling to assess partial directed coherence in multiple time series with significant instantaneous interactions[J].Biological Cybernetics,2010,103:387-400.
[14]Widjaja D,Montalto A,Vlemincx E,et al.Cardiorespiratory information dynamics during mental arithmetic and sustained attention.PLo S ONE,2015,10(6):e0129112.
[15]Heldt T,Oefinger MB,Hoshiyama M,et al.Circulatory response to passive and active changes in posture[J].Computers in Cardiology,2003,30:263-266.
[16]孙中伟,彭屹.用于QT间期检测的复合算法研究[J].生物医学工程与临床,2009,13(3):184-188.
[17]许亮,王星,孙中伟,等.心率变异性时频分析参数用于心电图ST段偏移时段的判别[J].中国生物医学工程学报,2008,27(1):23-27.
[18]Peng CK,Havlin S,Stanley HE,et al.Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series[J].Chaos,1995,5(1):82-87.
[19]Tulppo MP,Kiviniemi AM,Hautala AJ,et al.Physiological background of the loss of fractal heart rate[J].Circulation,2005,112:314-319.
[20]Orini M,Bail'on R,Mainardi L,et al.Characterization of dynamic interactions between cardiovascular signals by timefrequency coherence[J].IEEE Transactions on Biomedical Engineering,2012,59(3):663-673.
[21]Visnovcova Z,Mestanik M,Javorka M,et al.Complexity and time asymmetry of heart rate variability are altered in acute mental stress[J].Physiol Meas,2014,35(7):1319-1334.
[22]Faes L,Porta A,Nollo G.Information decomposition in bivariate systems:theory and application to cardiorespiratory dynamics[J].Entropy,2015,17(1):277-303.
[23]Wehrwein EA,Joyner MJ.Regulation of blood pressure by the arterial baroreflex and autonomic Nervous System[M].//Buijs RM,Swaab DF.Autonomic Nervous System.Amsterdam:Elsevier,2013:89-102.