Mutual information discloses relationship between hemodynamic variables in artificial heart-implanted dogs

Abstract

A mutual information (MI) method for assessment of the relationship between hemodynamic variables was proposed and applied to the analysis of heart rate (HR), arterial blood pressure (BP), and renal sympathetic nerve activity (RSNA) in artificial heart-implanted dogs to quantify correlation between these parameters. MI measures the nonlinear as well as linear dependence of two variables. Simulation studies revealed that this MI technique furnishes mathematical features well suited to the investigation of nonlinear dynamics such as the cardiovascular system and can quantify a relationship between two parameters. To constitute a model free of the natural heart, two pneumatically actuated ventricular assist devices were implanted as biventricular bypasses in acute canine experiments. RSNA was detected with the use of bipolar electrodes attached to the renal sympathetic nerve. Analysis of data during control revealed that correlation between HR and RSNA was higher than that between HR and BP and that between RSNA and BP ( P < 0.05). Although RSNA seemed to fluctuate noncorrelatedly with BP in higher pacing rates, the MI values between them disclosed their strong correlation. Surprisingly, correlation between RSNA and BP was stronger during a pacing rate of 60 beats/min than during higher pacing rates and control ( P < 0.05). It is suggested that the baroreflex system may be susceptible to pacing rates during the total artificial heart state. We calculated the time delay between HR and RSNA, between RSNA and BP, and between HR and BP by regarding a time delay at which the maximum MI value between each pair of parameters was given as a physiological delay. Our results indicate that RSNA leads BP, BP leads HR, and RSNA leads HR during control ( P < 0.05). We conclude that this method could provide a powerful means for measuring correlation of physiological variables.

Sudden Cardiac Death from the Perspective of Nonlinear Dynamics

“…We calculated the value of mutual information between each pair of these parameters, which quantifies both the linear and nonlinear correlations between two variables. We had previously determined the threshold between correlated and non-correlated data to be 0.047 30 . The values in these data .…”

Application of Heart Rate Variability Analysis to Electrocardiogram Recorded Outside the Driver's Awareness From an Automobile Steering Wheel

“…We calculated the mutual information values, according to an algorithm proposed by Fraser and Swinney 14 and used in our previous study. 15 For a couple of time series, {x(t)} and {y(t)}, we measured how dependent the values of y(t) were on the values of x(t). We made the assignment [s,q]=[x(t), y(t)] to consider a general coupled system (S,Q).…”

“…If the mutual information value was larger than or equal to 0.047, the correlation was taken to be strongly correlated, on the basis of our previous report. 15 …”

Synchrony analysis between blood pressure and sympathetic nerve signal inhibited by atrial receptor stimulation in Wistar rats

“…Using this linear dynamic approach the relationship between oscillations in BP and RSNA has been determined by measuring the correlation of these two variables such as coherence in the frequency domain (Brown et al 1994;Stauss et al 19970,b). However, this does not provide information o n whether the two variables are non-linearly correlated, and this is likely to be important because the cardiovascular system comprises non-linear dynamics from macro t o micro in scope (Osaka et al 1998). Therefore a method which measures the nonlinear trends of a cardiovascular time series is needed, such as quantification of asynchrony or conditional irregularity in interconnected (cardiovascular) networks.…”