Standard errors on respiratory mechanical parameters obtained by forced random excitation

Equations describing the standard errors of forced random impedance data and derived parameters in terms of various data collection and data processing factors were developed and verified. The equation indicate that to obtain reliable estimates: 1) 16 ensembles are adequate when coherence is greater than 0.9, and that 32 ensembles are adequate when the coherence is between 0.8 and 0.9; 2) the impedance of the bias tube should be at least two times the impedance of the respiratory system in the bandwidth of the applied noise; and 3) the spectrum should include at least 20 frequencies with at least 2 and preferably more below 10 Hz. Fortunately, all of these constraints can be satisfied with most subjects. This analysis also provides a basis for using weighted regression in estimating resistance, inertance, and compliance parameters, and for separating observed parameter variability into methodological and physiological components.     

Parameter Estipiates in a Five-Element Respiratory Mechanical Model

Using four sets of forced random noise impedance data from each of five normal subjects and five patients with obstructuve lung disease, we computed parameter estimates for a three-element series model and a five-element parallel compartment model. For normal subjects, the five-element model provided no better fit to the impedance data than did the simple series model. Estimates obtained from normal subjects using this three-element model were reasonable and reproducible within 25 percent. For all subjects with lung disease, the five-element model provided a significantly (p 0.05) better fit than the three-element model. Estimates for parameters representing central inertance and resistance, airway compliance, and peripheral resistance were reasonable and reproducible-within 18 percent. However, estimates for the compliance of the lung and chest wall were more variable since measured impedance appeared to be insensitive to this parameter in the frequency range used.     

Estimating Respiratory Mechanical Parameters in Parallel Compartment Models

Four iterative parameter estimation algorithms were used to obtain estimates in three parallel compartment models of the respiratory system. The stability of the parameter estimates and the agreement between the forced random noise impedance data and the model's response were evaluated for each algorithm-model combination. The combination of a two-stage simplex algorithm with a five element model provided the most stable parameter estimates and the second best fit to the data.     

Forced Oscillatory Parameters of the Canine Respiratory System with Altered Vagal Tone

Total respiratory impedance was obtained by forced oscillations ranging from 0.9 to 16 Hz in six anesthetized intubated dogs during a control period, after bilateral vagotomy, and during bilateral vagal stimulation. Values for forced oscillatory resistance, compliance, and inertance were calculated using regression analysis with a linear model. Mean values ±SD for the control period were 2.49 ±0.18 cmH2O ·L?1 ·s, 0.0254 ±0.0039 L ·cmH2O?1, and 0.0849 ±0.0055 cmH2O ·L?1·s2, respectively. These were similar to previously reported values. Vagotomy produced only small changes in these parameters. Vagal stimulation produced a 33 percent increase in resistance, a 20 percent decrease in compliance, and an 8 percent increase in inertance. Changes in resistance and compliance were consistent with reported effects in the literature. Thus, the transient mechanical changes induced by vagal stimulation can be characterized by this technique.     

Statistical Measures of Parameter Estimates from Models Fit to Respiratory Impedance Data: Emphasis on Joint Vanabilities

To describe respiratory mechanical impedance data, many investigators have proposed electromechanical models and then fit them to data using formal parameter estimation techniques. This approach has resulted in confusion as to how to interpret the resulting estimated values, and hence as to which model is most appropriate. A key cause of this confusion is that most studies rely on the quality of fit between the model and the data as the only measure of model validity rather than performing adequate statistical analysis of the parameter estimates themselves. This paper describes several statistical measures that should be applied to parameter estimates obtained from forced oscillation data. Specifically, we describe standard errors of the parameter estimates, confidence intervals for each parameter estimate, and the joint confidence region for the parameters. Much emphasis is placed on the joint confidence region which, unlike the interval, allows for simultaneous variations in parameters. The measures are applied to an often used six-element model for respiratory impedance data of dogs from 4 to 64 Hz. This application indicated that even when fitting data over this frequency range, parameter estimates are not well defined and the parameter estimated with least accuracy is airway resistance.     

Dynamic Characteristics of the Renal Arterial Microvasculature of the Dog Obtained by Simulation

To develop a measurement technique for microvascular stiffness and resistance, an iterative algorithm (NOLESQ) combining steepest descent and Taylor series (Gauss-Newton) methods was used to estimate model parameters to provide a minimal least squares error at 5-ms intervals of the predicted blood flow to the measured flow. Renal arterial pressure and flow data of high dynamic and static accuracy were used. The data were in blocks of 2500 values each (12.5-s samples). A reasonable fit was obtained with only three parameters: arterial conductance, compliance, and a steady pressure at or just beyond the glomerulae. Other models, incorporating large artery resistance, blood mass, pressure dependent conductance, and pressure dependent compliance were studied. Criteria for acceptance were reasonable values from prior knowledge of biological materials, similarity of parameter values between consecutive samples and between animals, convergence, and error of fit reduced to the noise level of the input data.     

Computation of Respiratory Resistance, Compliance, and Inertance from Forced Oscillatory Impedance Data

Relationships for computing total respiratory resistance, compliance, and inertance from the frequency dependence of forced oscillatory impedance are presented. These equations result by minimizing the sum squared difference between the experimental impedance data and the response of the model.     

A nonstatistical approach to estimating confidence intervals aboutmodel parameters: application to respiratory mechanics

Estimates of parameters obtained by fitting models to physiologic data are of little use unless accompanied by confidence intervals. The standard methods for estimating confidence intervals are statistical, and make the assumption that the fitted model accounts for all the deterministic variation in the data while the residuals between the fitted model and the data reflect only stochastic noise. In practice, this is frequently not the case, as one often finds the residuals to be systematically distributed about zero. In this paper, we develop an approach for assessing confidence in a parameter estimate when the order of the model is clearly less than that of the system being modeled. Our approach does not require a parameter to have a single value located within a region of confidence. Instead, we let the parameter value vary over the data set in such a way as to provide a good fit to the entire data set. We apply our approach to the estimation of the resistance of the respiratory system in which a simple model is fitted to measurements of tracheal pressure and flow by recursive multiple linear regression. The values of resistance required to achieve a good fit are represented as a modified histogram in which the contribution of a particular resistance value to the histogram is weighted by the amount of information used in its determination. Our approach provides parameter frequency distribution functions that convey the degree of confidence one may have in the parameter, while not being based on erroneous statistical assumptions.     

Estimating lung mechanics of dogs with unilateral lung injury

Extended least-squares algorithms using transpulmonary pressure and airway flow data from ventilatory waveforms were studied for their ability to track parameters of one- and two-compartment models of lung mechanics. A recursive extended least-squares algorithm with discounted measures estimated parameters of discrete-time models during synchronized intermittent mandatory ventilation. In tests on seven dogs developing oleic acid-induced unilateral hemorrhagic pulmonary edema, the one-compartment estimator responded rapidly and appropriately to changes in mechanics: compliance fell to 0.55 +/- 0.15 of its initial value and resistance rose by a factor of 1.8 +/- 0.5 in 3 h following injection of oleic acid. One-compartment parameter estimates revealed a difference between the airway resistance of inspiration and expiration. Two-compartment estimates were seldom physiologically plausible. The difference between inspiratory and expiratory resistance may have caused the two-compartment estimator to fail when applied to data from the entire respiratory cycle; when only expiratory data were used for estimation, the two-compartment estimates were meaningful. These estimates demonstrated increasing lung inhomogeneity after oleic acid was injected; at the end of 3 h, the ratio of the time constants of the two compartments ranged from 5 to 20 in six of the seven dogs. We conclude that the one- and two-compartment estimates may be combined to provide a meaningful assessment of lung mechanics.     

Sensorless control of salient PMSM with adaptive integrator and resistance online identification using strong tracking filter

This article presents a sensorless control approach of salient PMSM with an online parameter identifier. Adaptive Integrator is proposed and utilised for the estimation of active flux and rotor position. As a result, integrator overflow caused by DC offset is avoided. Meanwhile, an online stator resistance identification algorithm using strong tracking filter is employed, and the identified stator resistance is fed back to the estimating algorithm. Thus, the estimating algorithm can calculate the rotor position correctly. Simulations and experimental results validate the feasibility of both adaptive integrator and the parameter identification method.     

Systematic and random errors in the determination of respiratory impedance by means of the forced oscillation technique: a theoretical study

The measurement of the impedance of the respiratory system by means of the forced oscillation technique is disturbed by the breathing signal of the subject. The latter introduces systematic and random errors on the obtained impedance values. The size of these errors depends on the relative power of the breathing signal with respect to the forced oscillatory signal x2.     

Computer-controlled mechanical simulation of the artificially ventilated human respiratory system

A mechanical lung simulator can be used to simulate specific lung pathologies, to test lung-function equipment, and in instruction. A new approach to mechanical simulation of lung behavior is introduced that uses a computer-controlled active mechatronic system. The main advantage of this approach is that the static and dynamic properties of the simulator can easily be adjusted via the control software. A nonlinear single-compartment mathematical model of the artificially ventilated respiratory system has been derived and incorporated into the simulator control system. This model can capture both the static and dynamic compliance of the respiratory system as well as nonlinear flow-resistance properties. Parameters in this model can be estimated by using data from artificially ventilated patients. It is shown that the simulation model fits patient data well. This mathematical model of the respiratory system was then matched to a model of the available physical equipment (the simulator, actuators, and the interface electronics) in order to obtain the desired lung behavior. A significant time delay in the piston motion control loop has been identified, which can potentially cause oscillations or even instability for high compliance values. Therefore, a feedback controller based on the Smith-predictor scheme was developed to control the piston motion. The control system, implemented on a personal computer, also includes a user-friendly interface to allow easy parameter setting.     

高斯随机场参数的最小相对熵估计法

本文根据图像处理的需要，提出了高斯随机场参数的一种最小相对熵估计法，该方法利用模拟退火技术寻求概率分布的最小相对熵，文中给出了利用这种估计法对多分量混合－元高斯分布进行参数估计的实例。     

A Comparative Model Study of Respiratory Period Prediction on Men Exercising While Wearing Masks

Four competing models were tested to determine which model assumptions were necessary to successfully predict respiratory period. Data from a number of sources were used to calibrate the models. All models were found to be sensitive to data inhomogeneity. Minimization of total respiratory power and inspiratory power was found to yield nearly equivalent results except where a limitation on exhalation time forced expiratory power to be nonminimal. Inclusion of negative work is a necessary basic assumption. Mask resistance appears to be only half as effective as internal resistance in determining respiratory period. Values for respiratory system parameters were determined and these varied with conditions under which data were collected.     

Physiological interpretations based on lumped element models fit torespiratory impedance data: use of forward-inverse modeling

Respiratory impedance (Zrs) data at lower (less than 4 Hz) and higher (greater than 32 Hz) frequencies require more complicated inverse models than the standard series combination of a respiratory resistance, inertance, and compliance. In this paper, a forward-inverse modeling approach was used to provide insight on how the parameters in these more complicated inverse models reflect the true physiological system. Forward models are set up to incorporate explicit physiological and anatomical detail. Simulated forward data are then fit with identifiable inverse models and the parameter estimates related to the known detail in the forward model. It is shown that inverse fitting of low frequency data alone will not allow a distinction between frequency dependence due to airway inhomogeneities and frequency dependence due to tissue viscoelasticity. With higher frequency data, a forward model based on an asymmetric branching airways network was used to simulate Zrs from 0.1-128 Hz with increasing amounts of nonuniform peripheral airway obstruction. Here, inverse modeling is more amenable to sensibly separating estimates of airway and tissue properties. A key result, however, is that changes in the tissue parameters of an inverse model (which provides an excellent fit to Zrs data) will appropriately occur in response to inhomogeneous alterations in airway diameters only. The apparent altered tissue properties reflect the decreased communication of some tissue segments with the airway opening and not an explicit change at the tissue level. These phenomena present a substantial problem for the inverse modeler. Finally, inverse model fitting of low and high frequency Zrs data simultaneously with a single model is not helpful for extracting additional physiological detail. Instead, separate models should be applied to each frequency range.     

ARMA-cepstrum Recursion Algorithm for the Estimation of the MA Parameters of 2-D ARMA Models

This paper considers the problem of estimating the moving average (MA) parameters of a two-dimensional autoregressive moving average (2-D ARMA) model. To solve this problem, a new algorithm that is based on a recursion relating the ARMA parameters and cepstral coefficients of a 2-D ARMA process is proposed. On the basis of this recursion, a recursive equation is derived to estimate the MA parameters from the cepstral coefficients and the autoregressive (AR) parameters of a 2-D ARMA process. The cepstral coefficients are computed benefiting from the 2-D FFT technique. Estimation of the AR parameters is performed by the 2-D modified Yule–Walker (MYW) equation approach. The development presented here includes the formulation for real-valued homogeneous quarter-plane (QP) 2-D ARMA random fields, where data are propagated using only the past values. The proposed algorithm is computationally efficient especially for the higher-order 2-D ARMA models, and has the advantage that it does not require any matrix inversion for the calculation of the MA parameters. The performance of the new algorithm is illustrated by some numerical examples, and is compared with another existing 2-D MA parameter estimation procedure, according to three performance criteria. As a result of these comparisons, it is observed that the MA parameters and the 2-D ARMA power spectra estimated by using the proposed algorithm are converged to the original ones     

基于改进LS-ESPRIT算法的GTD模型参数估计与RCS重构

针对传统LS-ESPRIT算法在估计GTD模型参数时抗噪效果差,估计精度不高这一问题,该文提出了一种改进的LS-ESPRT算法,有效地提高了算法的参数估计性能与抗噪性。首先,根据雷达目标的回波数据构建Hankel矩阵;其次,采用核范数凸优化方法对上述Hankel矩阵进行降噪处理,得到低秩的重构Hankel矩阵;最后,利用传统的LS-ESPRIT算法对降噪后的数据进行处理,估计出GTD模型参数。基于改进算法与传统算法分别得到重构RCS,并针对不同带宽对参数估计精度的影响作以仿真探究。仿真结果表明,与传统LS-ESPRIT算法与传统TLS-ESPRIT算法相比,改进LS-ESPRIT算法的参数估计性能更高,抗噪性更强,且重构RCS的幅值与相角误差更小。对不同带宽下的参数估计精度也进行了探究,并得出:带宽越大,估计精度越高。     

阻变存储阵列的自动化测试系统

阻变存储器(RRAM)是一种新型的不挥发存储技术,研究阻变存储器阵列规模的存储性能以及可靠性问题是推进RRAM实用化的关键.目前通用的基于微控探针台的半导体参数分析的常规测量系统无法完成对阵列的自动化测试.利用半导体参数分析仪(4200-SCS)、开关矩阵以及相关外围电路搭建了一套针对阻变存储阵列的自动测试系统,实现了1MbitRRAM芯片的初始阻态分布的读取、初始化测试、存储单元的自动化编程/擦除操作.测试结果表明,该测试系统可以实现阻变存储阵列的自动化测试,为进一步工艺参数和编程算法的优化设计奠定基础.     

An EM Algorithm for Markov Modulated Markov Processes

An expectation–maximization (EM) algorithm for estimating the parameter of a Markov modulated Markov process in the maximum likelihood sense is developed. This is a doubly stochastic random process with an underlying continuous-time finite-state homogeneous Markov chain. Conditioned on that chain, the observable process is a continuous-time finite-state nonhomogeneous Markov chain. The generator of the observable process at any given time is determined by the state of the underlying Markov chain at that time. The parameter of the process comprises the set of generators for the underlying and conditional Markov chains. The proposed approach generalizes an earlier approach by RydÉn for estimating the parameter of a Markov modulated Poisson process.      

一种反演地下介质参数的新算法

在探地雷达实际工程应用中,介质参数(主要是介电常数)是使用者正确定位地下目标深度或介质层厚度所必需的数据。该文提出一种基于MUSIC谱估计的地下介质参数反演算法,该算法通过对LFMCW GPR回波信号的时延和幅度估计,计算出精确的介质参数并进一步得到正确的地下介质结构。仿真结果表明:该文提出的反演算法在反演精确度上远远优于传统的电磁反演算法。