Ramakrishna Mukkamala

System identification of closed-loop cardiovascular control mechanisms: diabetic autonomic neuropathy

We applied cardiovascular system identification (CSI) to characterize closed-loop cardiovascular regulation in patients with diabetic autonomic neuropathy (DAN). The CSI method quantitatively analyzes beat-to-beat fluctuations in noninvasively measured heart rate, arterial blood pressure (ABP), and instantaneous lung volume (ILV) to characterize four physiological coupling mechanisms, two of which are autonomically mediated (the heart rate baroreflex and the coupling of respiration, measured in terms of ILV, to heart rate) and two of which are mechanically mediated (the coupling of ventricular contraction to the generation of the ABP wavelet and the coupling of respiration to ABP). We studied 37 control and 60 diabetic subjects who were classified as having minimal, moderate, or severe DAN on the basis of standard autonomic tests. The autonomically mediated couplings progressively decreased with increasing severity of DAN, whereas the mechanically mediated couplings were essentially unchanged. CSI identified differences between the minimal DAN and control groups, which were indistinguishable based on the standard autonomic tests. CSI may provide a powerful tool for assessing DAN.

A single equivalent moving dipole model: an efficient approach for localizing sites of origin of ventricular electrical activation.

AbstractWe propose a new method for guiding catheter ablation procedures to abolish sites of origin of arrhythmias. This method models both cardiac electrical activity and current pulses delivered from the tip of the ablation catheter with a single equivalent moving dipole (SEMD). The SEMD parameters are obtained from analysis of body surface potentials. In this paper we examine the feasibility of this method by evaluating the performance of an inverse algorithm we developed to localize the SEMD from the surface potentials. In computer simulations realistic levels of measurement noise led to uncertainties in SEMD location ∼0.005 cm. Dipole orientation randomization contributed to increased uncertainty (0.04 cm) in SEMD location only when boundary effects were included. In ventricular pacing swine studies, we found that the SEMD model accurately accounted for electrocardiographic wave forms and that measurement noise led to an uncertainty of approximately 0.04 cm in the SEMD at 15 ms after the pacing spike. We have also found that the algorithm we developed to identify the SEMD parameters yielded positions for two spatially separated pacing sites that maintained their direction and were very close to their physical separation. These results suggest that the SEMD method may potentially be used to guide radio-frequency ablation procedures.

Introduction of computational models to PhysioNet

PhysioNet is a national research resource that provides experimental data sets and open-source software for their analysis. Computational modeling can complement studies of these experimental data sets so as to facilitate the advancement of physiologic research. Thus, in order to introduce computational models to PhysioNet, we have developed and posted a cardiovascular model designed for research that generates reasonable human pulsatile hemodynamic waveforms, cardiac output and venous return curves, and beat-to-beat variability. Some of the key features of the software include: 1) compatibility with PhysioNets open-source data analysis software; 2) online viewing and parameter updating as the data are being calculated; 3) off-line viewing after completion of the simulation; 4) pre-compiled Linux binaries; 5) open-source code that may be compiled on other platforms; and 6) an extensive users manual and software guide.

Effects of prolonged bed rest on the total peripheral resistance baroreflex

Orthostatic intolerance following prolonged exposure to microgravity continues to be a primary concern of the human space program. Reduced autonomic tone has been demonstrated to contribute to this phenomenon, and the heart rate baroreflex, in particular, has been repeatedly shown to be impaired. However, only the works of Yelle et al. (1996, 2002) have attempted to address the role of the total peripheral resistance (TPR) baroreflex, a potentially more significant contributor to blood pressure regulation. We applied a previously developed method for estimating the static gains of both the arterial and cardiopulmonary TPR baroreflexes to data obtained before and after 16 day bed rest. Reductions in the estimated static gains of the arterial (statistically significant) and cardiopulmonary TPR baroreflex were found after bed rest. This study supports the works of Yelle et al., which imply that the TPR baroreflex is reduced after spaceflight.

A computational model-based validation of Guyton's analysis of cardiac output and venous return curves

Guyton (1973) developed a popular approach for understanding the factors responsible for cardiac output (CO) regulation in which 1) the heart-lung unit and systemic circulation are independently characterized via CO and venous return (VR) curves, and 2) average CO and right atrial pressure (RAP) of the intact circulation are predicted by graphically intersecting the curves. However, this approach is virtually impossible to verify experimentally. We theoretically evaluated the approach with respect to a nonlinear, computational model of the pulsatile heart and circulation. We developed two sets of open circulation models to generate CO and VR curves, differing by the manner in which average RAP was varied One set applied constant RAPs, while the other set applied pulsatile RAPs. Accurate prediction of intact, average CO and RAP was achieved only by intersecting the CO and VR curves generated with pulsatile RAPs because of the pulsatility and nonlinearity (e.g., systemic venous collapse) of the intact model. The CO and VR curves generated with pulsatile RAPs were also practically independent. This theoretical study therefore supports the validity of Guytons graphical analysis.

A noninvasive method for total peripheral resistance baroreflex identification

We developed a noninvasive method for estimating the static gains of the arterial and cardiopulmonary total peripheral resistance (TPR) baroreflexes. The method involves a system identification analysis of beat-to-beat fluctuations in arterial blood pressure (ABP), cardiac output (CO), and stroke volume (SV) in order to identify two transfer functions relating CO fluctuations to ABP fluctuations and SV fluctuations to ABP fluctuations. The static gains of each of the TPR baroreflexes may then be computed from the static gains of the two identified transfer functions. In order to evaluate the method, we constructed a computer model of the human cardiovascular system. We applied the method to data generated from the computer model and found close agreement between the estimated and actual static gains of the model TPR baroreflexes. We also applied the method to experimental human data and obtained encouraging results. These results motivate the experimental validation of the method.

A model order selection criterion for the identification of physiologic systems

We propose a model order selection criterion for the identification of a linear regression model which can be an adequate representation of a resting physiologic system. The criterion, which is derived by estimating the mean squared parameter error weighted by the input data covariance matrix, is called WPE and reflects a trade-off between mean squared prediction error and model complexity. We compare the asymptotic performance of WPE with the widely used final prediction error (FPE). We also demonstrate through simulated and physiologic data that WPE minimization provides a more accurate and succinct characterization of system dynamics than FPE minimization. To our knowledge, WPE has not been previously proposed for model order selection.

A noninvasive method for characterizing ventricular diastolic filling dynamics

We developed a novel method for estimating a single parameter (/spl tau//sub D/) which summarizes the diastolic filling dynamics of the ventricle that governs the effects of diastolic filling time on stroke volume. The method involves the analysis of beat-to-beat fluctuations in heart rate, arterial blood pressure, and left ventricular stroke volume, each of which may be measured noninvasively in humans. In order to evaluate the method, we constructed a computational model of the human cardiovascular system. We applied the method to data generated from the computational model and found close agreement between the estimated and actual /spl tau//sub D/. We also demonstrated with computational model examples that the method could be utilized to monitor increasing degrees of cardiac tamponade and mitral stenosis, two cardiovascular disease processes which prohibit the ventricles from filling adequately. This model-based study motivates the experimental validation of the method.

Autonomic tone is impaired by prolonged bedrest and sympathetic tone before bedrest predicts tolerance to tilt after bedrest

Microgravity-induced orthostatic intolerance continues to be a primary concern of the human space program. Reduced autonomic tone has been shown to contribute to this phenomenon. However, the techniques that have been utilized for quantitating autonomic tone are limited. In this paper, we present an improved, noninvasive method for quantitating distinctly parasympathetic and /spl beta/-sympathetic tone and demonstrate its validity with respect to postural changes. We then apply the method to data collected from 20 human subjects before and after exposure to simulated microgravity (16-day head down bedrest). Statistical analyses show that parasympathetic and /spl beta/-sympathetic tone are both impaired by bedrest and reduced 0-sympathetic tone before bedrest can predispose subjects to presyncope following bedrest.

Do nonlinearities play a significant role in short term, beat-to-beat variability?

Numerous studies of short-term beat-to-beat variability in cardiovascular signals have not resolved the debate about the completeness of linear analysis techniques. This paper further evaluates the role of nonlinearities in short-term beat-to-beat variability. We compared linear autoregressive moving average (ARMA) and nonlinear neural network (NN) models for predicting instantaneous heart rate (HR) and mean arterial blood pressure (BP) from past HR and BP. To evaluate these models, we used HR and BP time series from the MIMIC database. Experimental results indicate that NN-based nonlinearities do not play a significant role and suggest that ARMA linear analysis techniques provide adequate characterization of the system dynamics responsible for generating short-term beat-to-beat variability.