Search PubMed⌕ Search

Biomedical subjects

K R Lutchen

Publications and source records attributed to K R Lutchen.

69 records · Page 4Linked to original sources
PubMed1985

Helium-oxygen breathing in severe chronic obstructive pulmonary disease.

The effect of breathing helium-oxygen (He-O2) mixtures was evaluated in 15 patients with severe chronic obstructive pulmonary disease (COPD). Gas exchange was assessed during quiet breathing at rest before and after 15 minutes of breathing 80 percent He-20 percent O2 mixtures in all patients. Functional residual capacity (FRC) determined during argon (Ar) washing studies fell significantly while breathing He-O2, but we did not find significant changes in minute ventilation, tidal volume, respiratory frequency, or inspiratory or expiratory timing. Eleven patients showed decreases in arterial PCO2 and CO2 excretion during resting breathing on He-O2. Expiratory flows were increased at a given lung volume during He-O2 breathing as expected. Apparently, mechanical work of breathing was decreased in patients with severe COPD while breathing He-O2, leading to a reduction in VCO2 and improvement in overall alveolar ventilation. These findings lend support to the therapeutic use of He-O2 under some conditions in patients with severe COPD.

Aged↗
PubMed1984

Mechanics and gas distribution in normal and obstructed lungs during tidal breathing.

Quantitative characteristics of the dynamic mechanical and gas distribution behavior in 6 normal subjects and 5 subjects with COPD were compared during tidal breathing. Transpulmonary pressure, total lung volume, flow, and N2 fraction at the mouth were measured while N2 was washed out from the lung. The washouts were performed at several frequencies and lung volumes. As an index of nonuniform mechanical behavior, we calculated the frequency variation in dynamic pulmonary compliance (CLdyn). Based on a moment analysis of the multibreath N2 washout, we calculated a mean dilution number (MDN) as an index of the inhomogeneity of alveolar gas distribution and mixing. At FRC or above FRC the CLdyn decreased much more with frequency for the COPD subjects than for the normal subjects. The MDN was also much greater in the presence of COPD. However, the frequency dependence of the MDN was small for both the normal and COPD subjects and uncorrelated with the frequency dependence of CLdyn. Because the multibreath N2 washout and the frequency dependence of CLdyn reflect different aspects of ventilation inhomogeneity, these two responses are unique.

Adolescent↗
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

Assessment of time-domain analyses for estimation of low-frequency respiratory mechanical properties and impedance spectra.

Time-domain estimation has been invoked for tracking of respiratory mechanical properties using primarily a simple single-compartment model containing a series resistance (Rrs) and elastance (Ers). However, owing to the viscoelastic properties of respiratory tissues, Rrs and Ers exhibit frequency dependence below 2 Hz. The goal of this study was to investigate the bias and statistical accuracy of various time-domain approaches with respect to model properties, as well as the estimated impedance spectra. Particular emphasis was placed on establishing the tracking capability using a standard step ventilation. A simulation study compared continuous-time versus discrete-time approaches for both the single-compartment and two-compartment models. Data were acquired in four healthy humans and two dogs before and after induced severe pulmonary edema while applying sinusoidal and standard ventilator forcing. Rrs and Ers were estimated either by the standard Fast Fourier Transform (FFT) approach or by a time-domain least square estimation. Results show that the continuous-time model form produced the least bias and smallest parameter uncertainty for a single-compartment analysis and is quite amenable for reliable on-line tracking. The discrete-time approach exhibits large uncertainty and bias, particularly with increasing noise in the flow data. In humans, the time-domain approach produced smooth estimates of Rrs and Ers spectra, but they were statistically unreliable at the lower frequencies. In dogs, both the FFT and time-domain analysis produced reliable and stable estimates for Rrs or Ers spectra for frequencies out to 2 Hz in all conditions. Nevertheless, obtaining stable on-line parameter estimates for the two-compartment viscoelastic models remained difficult. We conclude that time-domain analysis of respiratory mechanics should invoke a continuous-time model form.

Animals↗
PubMedDate not supplied

Harmonic distortion from nonlinear systems with broadband inputs: applications to lung mechanics.

We present a simple index, extended harmonic distortion (kd), to represent the degree of system nonlinearity under sparse pseudorandom noise inputs (SPRN). The frequencies in a SPRN waveform are neither harmonics nor sums or differences of the other component frequencies. Expressed by percentage, the kd is the square root of the ratio of output power at non-input frequencies to the total output power. We evoke three simple corrections to recover the true kd under imperfect SPRN inputs. Simulations on two block-structured nonlinear models (Wiener and Hammerstein) demonstrate the necessity and effectiveness of these corrections especially for the Wiener-type nonlinearity. By applying kd to pressure-flow data of dog lungs, we found that the nonlinear harmonic interactions from a lung arise primarily from its tissues most likely the processes governing the tissue stiffness. This nonlinearity, assessed from kd, is stronger at higher tidal volumes and enhanced (but to a lesser degree) during bronchoconstriction. We conclude that since the kd approach avoids the necessity for multiple-input measurements and lengthy data records, it may be useful for tracking the dynamic variations in nonlinearities of a physiological system.

Animals↗
PubMedDate not supplied

Time series versus Fourier transform methods for estimation of respiratory impedance spectra.

Most current techniques to estimate respiratory system mechanical input impedance spectra (Zrs) use digitally created (Fourier transform (FFT) based) random noise. Recent Zrs data reported from 0.1-4 Hz and above 32 Hz display sharper, more distinct spectral features. When expressing Zrs as a power spectral density (PSD), such features suggest the application of a time series spectral estimation approach. Here, a simulation study was performed to compare the quality of impedance (PSD and Zrs) estimates from the time series technique to those from the FFT approach in the presence of measurement noise. Random noise pressure and flow time domain sequences were simulated for two different networks, one which exhibits impedance features reported from 0.1-4 Hz and one which exhibits impedance features reported above 32 Hz. In the time series method, autoregressive (AR), moving average (MA), and autoregressive-moving average (ARMA) models were fit to the pressure and flow sequences separately. The estimated PSD and complex Zrs spectra were compared to the true spectra calculated from the models. Results show that the time series PSD estimates were reasonable even in the presence of additive measurement noise. Conversely, with additive noise, the time series estimates of the complex Zrs showed a negative real part which is physiologically inappropriate. This occurs because of the loss of phase information inherent to the time series approach. Regardless of measurement noise, the FFT estimates of impedance were always close to the true impedance and always superior to the time series estimates. We conclude that an accurate estimate of the PSD or complex Zrs spectra from digitally created FFT-based random noise is best obtained using the traditional FFT method.

Airway Resistance↗
PubMedDate not supplied

Sensitivity analysis for evaluating nonlinear models of lung mechanics.

We present a combined theoretical and numerical procedure for sensitivity analyses of lung mechanics models that are nonlinear in both state variables and parameters. We apply the analyses to a recently proposed nonlinear lung model which incorporates a wide range of potential nonlinear identification conditions including nonlinear viscoelastic tissues, airway inhomogeneities via a parallel airway resistance distribution function, and a nonlinear block-structure paradigm. Additionally, we examine a system identification procedure which fits time- and frequency-domain data simultaneously. Model nonlinearities motivate sensitivity analyses involving numerical approximation of sensitivity coefficients. Examination of the normalized sensitivity coefficients provides direct insight on the relative importance of each model parameter, and hence the respective mechanism. More formal quantification of parameter uniqueness requires approximation of the paired and multidimensional parameter confidence regions. Combined with parameter estimation, we use the sensitivity analyses to justify tissue nonlinearities in modeling of lung mechanics for healthy and airway constricted conditions, and to justify both airway inhomogeneities and tissue nonlinearities during bronchoconstriction. The tools in this paper are general and can be applied to a wide class of nonlinear models.

Airway Resistance↗
PubMedDate not supplied

Technique to determine inspiratory impedance during mechanical ventilation: implications for flow limited patients.

We present the design of an enhanced ventilator waveform (EVW) for routine measurement of inspiratory resistance (R) and elastance (E) spectra in ventilator-dependent and/or severely obstructed flow-limited patients. The EVW delivers an inspiratory tidal volume of fresh gas with a flow pattern consisting of multiple sinusoids from 0.156 to 8.1 Hz and permits a patient-driven exhalation to the atmosphere or positive end-expiratory pressure. Weighted least-squares estimates of the coefficients in a sinusoidal series approximation of the EVW inspirations yielded inspiratory R and E spectra. We first validated the EVW approach using simulated pressure and flow data under different physiological conditions, noise levels, and harmonic distortions. We then applied the EVW in four intubated patients during anesthesia and paralysis: two with mild airway obstruction and two with severe emphysema and flow limitation. While the level of inspiratory R was similar in both groups of patients, the inspiratory E of the emphysematous patients demonstrated a pronounced frequency-dependent increase consistent with severe peripheral airway obstruction. We conclude that the EVW offers a potentially practical and efficient approach to monitor lung function in ventilator-dependent patients, especially those with expiratory flow limitation.

Airway Resistance↗
PubMedDate not supplied

How heterogeneous bronchoconstriction affects ventilation distribution in human lungs: a morphometric model.

Convective dependent flow heterogeneities associated with airways proximal to the acini are the dominant cause of abnormal ventilation distribution during induced bronchoconstriction (Verbanck, S., D. Schuermans, A. Van Muylem, M. Paira, M. Noppen, and W. Vincken. Ventilation distribution during histamine provocation. J. Appl. Physiol. 83:1907-1916, 1997). We applied a morphometric model of the human lung to predict flow distributions among the acini during heterogeneous bronchoconstriction and relate these distributions to impairments in the mechanical properties of the lung. The model has an asymmetrical branching airway system. Heterogeneous constriction was invoked by defining an airway constriction distribution with a mean (mu) and coefficient of variation (CV) and either a Gaussian or log normal distribution. The lung resistance (RL) and elastance (EL) were most sensitive to severely heterogeneous constriction that produced a few highly constricted or closed airways dispersed randomly throughout the periphery. Ventilation distribution in the healthy lung was effectively homogeneous over the frequency range of 0.1-5.0 Hz. With homogeneous or mildly heterogeneous constriction (CV< or =20%) ventilation remained fairly homogeneous at low frequencies (< or =0.1 Hz) but rapidly became heterogeneous as frequency increased. Conversely, a low mean but severely heterogeneous constriction that produced random airway closure produced abnormal ventilation distribution in most acini at all frequencies, and some acini received up to 25 times the normal ventilation. This suggests that certain forms of heterogeneity can lead to shear induced lung injury even at common mechanical ventilation rates.

Animals↗
PubMedDate not supplied

A frequency domain approach to nonlinear and structure identification for long memory systems: application to lung mechanics.

From the input-output point of view, many nonlinear biological systems display long memory characteristics which can become a critical issue using nonparametric time-domain kernel identification due to inevitable truncation of memory length. To avoid these limitations, we present an alternative approach in the frequency domain with application to lung mechanics. Generally, if the system is excited with a periodic wave form, the response will approach a steady state which dominates the long memory transients. Thus, we hypothesized that the kernels at discrete frequencies will not be significantly affected by memory truncation. To test this, we extended the frequency kernel analysis of Victor and Shapley (Biophys. J. 29:459-484, 1980) to a nonwhite input spectrum and developed a new structure test in the frequency domain to differentiate between Wiener and Hammerstein models. These techniques were applied to measured pressure-flow data of isolated lung lobes. The results showed that (1) the important nonlinearities in the pressure-flow relation are of second order, (2) the frequency kernels of the lobes were similar for flat and ventilatory-like input spectra, and (3) the structure test strongly suggested that the pressure-flow relationship during tidal-like excursions is consistent with a Wiener structure.

Animals↗
PubMedDate not supplied

Parametric and nonparametric nonlinear system identification of lung tissue strip mechanics.

Lung parenchyma is a soft biological material composed of many interacting elements such as the interstitial cells, extracellular collagen-elastin fiber network, and proteoglycan ground substance. The mechanical behavior of this delicate structure is complex showing several mild but distinct types of nonlinearities and a fractal-like long memory stress relaxation characterized by a power-law function. To characterize tissue nonlinearity in the presence of such long memory, we investigated the robustness and predictive ability of several nonlinear system identification techniques on stress-strain data obtained from lung tissue strips with various input wave forms. We found that in general, for a mildly nonlinear system with long memory, a nonparametric nonlinear system identification in the frequency domain is preferred over time-domain techniques. More importantly, if a suitable parametric nonlinear model is available that captures the long memory of the system with only a few parameters, high predictive ability with substantially increased robustness can be achieved. The results provide evidence that the first-order kernel of the stress-strain relationship is consistent with a fractal-type long memory stress relaxation and the nonlinearity can be described as a Wiener-type nonlinear structure for displacements mimicking tidal breathing.

Algorithms↗
PubMedDate not supplied

Sensitivity analysis of kernel estimates: implications in nonlinear physiological system identification.

Many techniques have been developed for the estimation of the Volterra/Wiener kernels of nonlinear systems, and have found extensive application in the study of various physiological systems. To date, however, we are not aware of methods for estimating the reliability of these kernels from single data records. In this study, we develop a formal analysis of variance for least-squares based nonlinear system identification algorithms. Expressions are developed for the variance of the estimated kernel coefficients and are used to place confidence bounds around both kernel estimates and output predictions. Specific bounds are developed for two such identification algorithms: Korenberg's fast orthogonal algorithm and the Laguerre expansion technique. Simulations, employing a model representative of the peripheral auditory system, are used to validate the theoretical derivations, and to explore their sensitivity to assumptions regarding the system and data. The simulations show excellent agreement between the variances of kernel coefficients and output predictions as estimated from the results of a single trial compared to the same quantities computed from an ensemble of 1000 Monte Carlo runs. These techniques were validated with white and nonwhite Gaussian inputs and with white Gaussian and nonwhite non-Gaussian measurement noise on the output, provided that the output noise source was independent of the test input.

Algorithms↗
PubMedDate not supplied

Factors affecting Volterra kernel estimation: emphasis on lung tissue viscoelasticity.

The goal of this study is to quantitatively investigate how the memory length, order of nonlinearity, type of input, and measurement noise can affect the identification of the Volterra kernels of a nonlinear viscoelastic system, and hence the inference on system structure. We explored these aspects with emphasis on nonlinear lung tissue mechanics around breathing frequencies, where the memory length issue can be critical and a ventilatory input is clinically demanded. We adopted and examined Korenberg's fast orthogonal algorithm since it is a least-squares technique that does not demand white Gaussian noise input and makes no presumptions on the kernel shape and system structure. We then propose a memory autosearch method, which incorporates Akaike's final production error criterion into Korenberg's fast orthogonal algorithm to identify the memory length simultaneously with the kernels. Finally, we designed a special ventilatory flow input and evaluated its potential for the kernel identification of the nonlinear systems requiring oscillatory forcing. We found that the long memory associated with soft tissue viscoelasticity may prohibit correct identification of the higher-order kernels of the lung. However, the key characteristics of the first-order kernel may be revealed through averaging over multiple experiments and estimations.

Algorithms↗