Biomedical subjects

D J Christini

Publications and source records attributed to D J Christini.

8 recordsLinked to original sources

PubMed2001

Nonlinear-dynamical arrhythmia control in humans.

Nonlinear-dynamical control techniques, also known as chaos control, have been used with great success to control a wide range of physical systems. Such techniques have been used to control the behavior of in vitro excitable biological tissue, suggesting their potential for clinical utility. However, the feasibility of using such techniques to control physiological processes has not been demonstrated in humans. Here we show that nonlinear-dynamical control can modulate human cardiac electrophysiological dynamics by rapidly stabilizing an unstable target rhythm. Specifically, in 52/54 control attempts in five patients, we successfully terminated pacing-induced period-2 atrioventricular-nodal conduction alternans by stabilizing the underlying unstable steady-state conduction. This proof-of-concept demonstration shows that nonlinear-dynamical control techniques are clinically feasible and provides a foundation for developing such techniques for more complex forms of clinical arrhythmia.

Adult↗

PubMed2001

Restricted feedback control of one-dimensional maps.

Dynamical control of biological systems is often restricted by the practical constraint of unidirectional parameter perturbations. We show that such a restriction introduces surprising complexity to the stability of one-dimensional map systems and can actually improve controllability. We present experimental cardiac control results that support these analyses. Finally, we develop new control algorithms that exploit the structure of the restricted-control stability zones to automatically adapt the control feedback parameter and thereby achieve improved robustness to noise and drifting system parameters.

Journal Article↗

PubMed2001

Direct biologically based biosensing of dynamic physiological function.

Dynamic regulation of biological systems requires real-time assessment of relevant physiological needs. Biosensors, which transduce biological actions or reactions into signals amenable to processing, are well suited for such monitoring. Typically, in vivo biosensors approximate physiological function via the measurement of surrogate signals. The alternative approach presented here would be to use biologically based biosensors for the direct measurement of physiological activity via functional integration of relevant governing inputs. We show that an implanted excitable-tissue biosensor (excitable cardiac tissue) can be used as a real-time, integrated bioprocessor to analyze the complex inputs regulating a dynamic physiological variable (heart rate). This approach offers the potential for long-term biologically tuned quantification of endogenous physiological function.

Animals↗

PubMed2001

Complex AV nodal dynamics during ventricular-triggered atrial pacing in humans.

In vitro experiments have shown that the complexity of atrioventricular nodal (AVN) conduction dynamics increases with heart rate. Although complex AVN dynamics (e.g., alternans) have been observed clinically, human AVN dynamics during rapid pacing have not been systematically investigated. We studied such dynamics during ventricular-triggered atrial pacing in 37 patients with normal AVN function (18 patients with dual AVN pathway physiology and 19 patients without). Alternans, which always resulted from single pathway conduction, occurred in 18 patients. In 16 patients (3 of whom also had alternans), quasisinusoidal AVN conduction oscillations occurred (mean frequency 0.02 Hz); such oscillations have not been previously reported. There were no significant differences in the dynamics for patients with or without dual AVN pathways. To illuminate the governing dynamic mechanism, a second atrial pacing trial was performed on 12 patients after autonomic blockade. Blockade facilitated alternans but inhibited oscillations. This study suggests that rapid AVN excitation in vivo can lead to autonomically mediated AVN conduction oscillations or single pathway alternans that are a function of inherent nonlinear dynamic AVN tissue properties.

Atrioventricular Node↗

PubMed2000

Adaptive estimation and control method for unstable periodic dynamics in spike trains.

Dynamical control of excitable biological systems is often complicated by the difficult and unreliable task of precontrol identification of unstable periodic orbits (UPO's). Here we show that, for both chaotic and nonchaotic systems, UPO's can be located, and their dynamics characterized, during control. Tracking of system nonstationarities emerges naturally from this approach. Such a method is potentially valuable for the control of spike trains of excitable biological systems, for which precontrol UPO identification is often impractical, and nonstationarities (natural or stimulation induced) are common.

Feedback↗

PubMed1995

Application of linear and nonlinear time series modeling to heart rate dynamics analysis.

The linear autoregressive (AR) model is often used to investigate the pathophysiologic mechanisms controlling heart rate (HR) dynamics. This study implemented parametric models new to this field to determine if a more appropriate HR dynamics modeling structure exists. The linear AR and autoregressive-moving average (ARMA) models, and the nonlinear polynomial autoregressive (PAR) and bilinear (BL) models were fit to instantaneous HR time series obtained from nine subjects in the supine position. Model orders were determined by the Akaike Information Criteria (AIC). Model residual variance was used as the primary intermodel comparison criterion, with significance evaluated by a chi 2 distributed statistic. The BL model best represented the HR dynamics, as its residual variance was significantly (p < 0.05) smaller than that of the corresponding AR model for nine out of nine data sets. In all cases, the BL model had a smaller residual variance than either the ARMA or PAR models. The bilinear model was ineffective at data forecasting, however, we show that this cannot reflect BL model validity because poor prediction is inherent to the BL model structure. The apparent superiority of the nonlinear bilinear model suggests that future heart rate dynamics studies should put greater emphasis on nonlinear analyses.

Adult↗

PubMedDate not supplied

Influence of autoregressive model parameter uncertainty on spectral estimates of heart rate dynamics.

Linear autoregressive (AR) model-based heart rate (HR) spectral analysis has been widely used to study HR dynamics. Owing to system and measurement noise, the parameters of an AR model have intrinsic statistical uncertainty. In this study, we evaluate how this AR parameter uncertainty can translate to uncertainty in HR power spectra. HR time series, obtained from seven subjects in supine and standing positions, were fitted to AR models by least squares minimization via singular value decomposition. Spectral uncertainty due to inexact parameter estimation was assessed through a Monte Carlo study in which the AR model parameters were varied randomly according to their Gaussian distributions. Histogram techniques were used to evaluate the distribution of 50,000 AR spectral estimates of each HR time series. These Monte Carlo uncertainties were found to exceed those predicted by previous theoretical approximations. It was determined that the uncertainty of AR HR spectral estimates, particularly the locations and magnitudes of spectral peaks, can often be large. The same Monte Carlo analysis was applied to synthetic AR time series and found levels of spectral uncertainty similar to that of the HR data, thus suggesting that the results of this study are not specific to experimental HR data. Therefore, AR spectra may be unreliable, and one must be careful in assigning pathophysiological origins to specific spectral features of any one spectrum.

Adult↗

PubMedDate not supplied

Practical real-time computing system for biomedical experiment interface.

Many biomedical experiments require a precisely timed real-time (RT) computer interface. Because commonly used desktop operating systems are inherently non-real-time, real-time laboratory computer systems are often based on outdated DOS software or expensive proprietary real-time operating systems. Here we discuss a real-time computing system, based on the free RT-LINUX operating system, which we have developed for adaptive pacing control in a clinical cardiac electrophysiology laboratory. This powerful, flexible, and inexpensive system demonstrates that RT-LINUX is well suited for real-time biomedical experiment interface.

Algorithms↗