Biomedical subjects

Ki H Chon

Publications and source records attributed to Ki H Chon.

6 recordsLinked to original sources

PubMed2003

Low-order chaos in sympathetic nerve activity and scaling of heartbeat intervals.

The mechanism of 1/f scaling of heartbeat intervals remains unknown. We recorded heartbeat intervals, sympathetic nerve activity, and blood pressure in conscious rats with normal or high blood pressure. Using nonlinear analyses, we demonstrate that the dynamics of this system of three variables is low-order chaos, and that sympathetic nerve activity leads to heartbeat interval and blood pressure changes. It is suggested that impaired regulation of blood pressure by sympathetic nerve activity is likely to cause experimentally observable steeper scaling of heartbeat intervals in hypertensive (high blood pressure) rats.

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PubMed2003

Blockade of brain stem gap junctions increases phrenic burst frequency and reduces phrenic burst synchronization in adult rat.

Recent investigations have examined the influence of gap junctional communication on generation and modulation of respiratory rhythm and inspiratory motoneuron synchronization in vitro using transverse medullary slice and en bloc brain stem-spinal cord preparations obtained from neonatal (1-5 days postnatal) mice. Gap junction proteins, however, have been identified in both neurons and glia in brain stem regions implicated in respiratory control in both neonatal and adult rodents. Here, we used an in vitro arterially perfused rat preparation to examine the role of gap junctional communication on generation and modulation of respiratory rhythm and inspiratory motoneuron synchronization in adult rodents. We recorded rhythmic inspiratory motor activity from one or both phrenic nerves before and during pharmacological blockade (i.e., uncoupling) of brain stem gap junctions using carbenoxolone (100 microM), 18alpha-glycyrrhetinic acid (25-100 microM), 18beta-glycyrrhetinic acid (25-100 microM), octanol (200-300 microM), or heptanol (200 microM). During perfusion with a gap junction uncoupling agent, we observed an increase in the frequency of phrenic bursts (~95% above baseline frequency; P < 0.001) and a decrease in peak amplitude of integrated phrenic nerve discharge (P < 0.001). The increase in frequency of phrenic bursts resulted from a decrease in both T(I) (P < 0.01) and T(E) (P < 0.01). In addition, the pattern of phrenic nerve discharge shifted from an augmenting discharge pattern to a "bell-shaped" or square-wave discharge pattern in most experiments. Spectral analyses using a fast Fourier transform (FFT) algorithm revealed a reduction in the peak power of both the 40- to 50-Hz peak (corresponding to the MFO) and 90- to 110-Hz peak (corresponding to the HFO) although spurious higher frequency activity (> or =130 Hz) was observed, suggesting an overall loss or reduction in inspiratory-phase synchronization. Although additional experiments are required to identify the specific brain stem regions and cell types (i.e., neurons, glia) mediating the observed modulations in phrenic motor output, these findings suggest that gap junction communication modulates generation of respiratory rhythm and inspiratory motoneuron synchronization in adult rodents in vitro.

Age Factors↗

PubMed2002

Time-varying properties of renal autoregulatory mechanisms.

In order to assess the possible time-varying properties of renal autoregulation, time-frequency and time-scaling methods were applied to renal blood flow under broad-band forced arterial blood pressure fluctuations and single-nephron renal blood flow with spontaneous oscillations obtained from normotensive (Sprague-Dawley, Wistar, and Long-Evans) rats, and spontaneously hypertensive rats. Time-frequency analyses of normotensive and hypertensive blood flow data obtained from either the whole kidney or the single-nephron show that indeed both the myogenic and tubuloglomerular feedback (TGF) mechanisms have time-varying characteristics. Furthermore, we utilized the Renyi entropy to measure the complexity of blood-flow dynamics in the time-frequency plane in an effort to discern differences between normotensive and hypertensive recordings. We found a clear difference in Renyi entropy between normotensive and hypertensive blood flow recordings at the whole kidney level for both forced (p < 0.037) and spontaneous arterial pressure fluctuations (p < 0.033), and at the single-nephron level (p < 0.008). Especially at the single-nephron level, the mean Renyi entropy is significantly larger for hypertensive than normotensive rats, suggesting more complex dynamics in the hypertensive condition. To further evaluate whether or not the separation of dynamics between normotensive and hypertensive rats is found in the prescribed frequency ranges of the myogenic and TGF mechanisms, we employed multiresolution wavelet transform. Our analysis revealed that exclusively over scale ranges corresponding to the frequency intervals of the myogenic and TGF mechanisms, the widths of the blood flow wavelet coefficients fall into disjoint sets for normotensive and hypertensive rats. The separation of the scales at the myogenic and TGF frequency ranges is distinct and obtained with 100% accuracy. However, this observation remains valid only for the whole kidney blood pressure/flow data. The results suggest that understanding of the time-varying properties of the two mechanisms is required for a complete description of renal autoregulation.

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PubMed2002

A stochastic nonlinear autoregressive algorithm reflects nonlinear dynamics of heart-rate fluctuations.

Current methods for detecting nonlinear determinism in a time series require long and stationary data records, as most of them assume that the observed dynamics arise only from the internal, deterministic workings of the system, and the stochastic portion of the signal (the noise component) is assumed to be negligible. To explicitly account for the stochastic portion of the data we recently developed a method based on a stochastic nonlinear autoregressive (SNAR) algorithm. The method iteratively estimates nonlinear autoregressive models for both the deterministic and stochastic portions of the signal. Subsequently, the Lyapunov exponents (LE) are calculated for the estimated models in order to examine if nonlinear determinism is present in the deterministic portion of the fitted model. To determine if nonlinear dynamic analysis of heart-rate fluctuations can be used to assess arrhythmia susceptibility by predicting the outcome of invasive cardiac electrophysiologic study (EPS), we applied the SNAR algorithm to noninvasively measured resting sinus-rhythm heart-rate signals obtained from 16 patients. Our analysis revealed that a positive LE was highly correlated to a patient with a positive outcome of EPS. We found that the statistical accuracy of the SNAR algorithm in predicting the outcome of EPS was 88% (sensitivity=100%, specificity=75%, positive predictive value=80%, negative predictive value=100%, p=0.0019). Our results suggest that the SNAR algorithm may serve as a noninvasive probe for screening high-risk populations for malignant cardiac arrhythmias.

Adult↗

PubMed2002

A new approach to closed-loop linear system identification via a vector autoregressive model.

A new vector autoregressive (VAR) model algorithm is developed for closed-loop identification. The new VAR approach is an extension of a recently developed algorithm, named the optimal parameter search (OPS), thus, we call the new technique VOPS, for vector OPS. Monte Carlo simulations of closed-loop systems were performed to compare the performance of VOPS to the widely utilized vector least squares (VLS) and vector fast orthogonal search (VFOS) approaches. In addition, we examined the effect on parameter estimates obtained via open-loop identification techniques, when using data from closed-loop systems. Comparative results show that both the VOPS and VFOS algorithms produce far more accurate parameter estimates than does the VLS. Furthermore, open-loop identification via univariate OPS and to a lesser extent univariate FOS for closed-loop systems, does not adversely affect the accuracy of the parameter estimates. An open-loop identification via the univariate least-squares method for closed-loop systems showed the most deleterious effect on the accuracy of the parameter estimates. In addition to developing the VOPS algorithm, we also developed approaches termed constrained OPS (COPS) and constrained FOS (CFOS). For closed-loop systems considered in this paper, both COPS and CFOS resulted in more accurate parameter estimates (less biased and more efficient) than did VLS, VFOS, and VOPS.

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PubMedDate not supplied

A robust time-varying identification algorithm using basis functions.

We extend a recently developed time invariant (TIV) model order search criterion named the optimal parameter search algorithm (OPS) for identification of time varying (TV) autoregressive (AR) and autoregressive moving average (ARMA) models. Using the TV algorithm is facilitated by the fact that expanding each TV coefficient onto a finite set of basis sequences permits TV parameters to become TIV. Taking advantage of this TIV feature of expansion parameters exploits the features of the OPS, which has been shown to provide accurate model order selection as well as extraction of only the significant model terms. Another advantage of the new algorithm is its ability to discriminate insignificant basis sequences thereby reducing the number of expansion parameters to be estimated. Due to these features, the resulting algorithm can accurately estimate TV AR or ARMA models and determine their orders. Indeed, comparison via computer simulations of AR models between the proposed method and one of the well-known iterative methods, recursive least squares, shows the greater capability of the new method to track TV parameters. Furthermore, application of the new method to experimentally obtained renal blood flow signals shows that the new method provides higher-resolution time-varying spectral capability than does the short-time Fourier transform (STFT), concomitant with fewer spurious frequency peaks than obtained with the STFT spectrogram.

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