Estimation of Respiratory Nasal Pressure and Flow Rate Signals Using Different Respiratory Sound Features

BackgroundRespiratory sounds are associated with the flow rate, nasal flow pressure, and physical characteristics of airways. In this study, we aimed to develop the flow rate and nasal flow pressure estimation models for the clinical application, and find out the optimal feature set for estimation to achieve the optimal model performance.MethodsRespiratory sounds and flow rate were acquired from nine healthy volunteers. Respiratory sounds and nasal flow pressure were acquired from twenty-three healthy volunteers. Four types of respiratory sound features were extracted for flow rate and nasal flow pressure estimation using different estimation models. Effects of estimations using these features were evaluated using Bland-Altman analysis, estimation error, and respiratory sound feature calculation time. Besides, expiratory and inspiratory phases divided estimation errors were compared with united estimation errors.ResultsThe personalized logarithm model was selected as the optimal flow rate estimation model. Respiratory nasal flow pressure estimation based on this model was also performed. For the four different respiratory sound features, there is no statistically significant difference in flow rate and pressure estimation errors. LogEnvelope was, therefore, chosen as the optimal feature because of the lowest computational cost. In addition, for any type of respiratory sound feature, no statistically significant difference was observed between divided and united estimation errors (flow rate and pressure).ConclusionRespiratory flow rate and nasal flow pressure can be estimated accurately using respiratory sound features. Expiratory and inspiratory phases united estimation using respiratory sounds is a more reasonable estimation method than divided estimation. LogEnvelope can be used for this united respiratory flow rate and nasal flow pressure estimation with minimum computational cost and acceptable estimation error.     

Product Cum Difference Method of Estimation Using two Auxiliary Variables

Product method of estimation (MURTHY, 1964) using supplementary information on an auxiliary variable having high negative correlation with the main variable under our study, is well known. In this paper, we propose product-cum-difference method of estimation for the population total and the product of population parameters when supplementary information is available on two auxiliary variables. A comparison of product-cum-difference method of estimation with the usual product method of estimation using single auxiliary character and the estimators by SINGH (1965) for the estimation of product of population parameters has been made along with an empirical study.     

Amélioration de l’estimation du rythme cardiaque fœtal en utilisant les signaux Doppler directionnels et YIN

Techniques dedicated to the fetal heart rate detection identify the patterns that repeat themselves over the time. The heart rate estimation is algorithmically similar to the estimation of the fundamental frequency (pitch) of voice signals. The new YIN technique introduced for the estimation of the fundamental frequency is applied to fetal heart rate estimation from the directional Doppler signals. We compare the performances in terms of probability of detection and accuracy of the estimation of the technique YIN with those of the cross-correlation, implemented in the Oxford SONICAID? monitors. A better detection probability and accuracy of estimation of fetal heart rate was obtained in case of YIN.     

A Highly Accurate Pixel-Based FRAP Model Based on Spectral-Domain Numerical Methods

We introduce a new, to our knowledge, numerical model based on spectral methods for analysis of fluorescence recovery after photobleaching data. The model covers pure diffusion and diffusion and binding (reaction-diffusion) with immobile binding sites, as well as arbitrary bleach region shapes. Fitting of the model is supported using both conventional recovery-curve-based estimation and pixel-based estimation, in which all individual pixels in the data are utilized. The model explicitly accounts for multiple bleach frames, diffusion (and binding) during bleaching, and bleaching during imaging. To our knowledge, no other fluorescence recovery after photobleaching framework incorporates all these model features and estimation methods. We thoroughly validate the model by comparison to stochastic simulations of particle dynamics and find it to be highly accurate. We perform simulation studies to compare recovery-curve-based estimation and pixel-based estimation in realistic settings and show that pixel-based estimation is the better method for parameter estimation as well as for distinguishing pure diffusion from diffusion and binding. We show that accounting for multiple bleach frames is important and that the effect of neglecting this is qualitatively different for the two estimation methods. We perform a simple experimental validation showing that pixel-based estimation provides better agreement with literature values than recovery-curve-based estimation and that accounting for multiple bleach frames improves the result. Further, the software developed in this work is freely available online.     

Comparison of quantitative light microscopy techniques used in diet studies of detritus-consuming omnivores

The assumptions and performances of three light microscope techniques for measuring the amount of detritus in the diets of omnivorous fish were compared: area estimation, volume estimation with subsequent estimation of weight, and weight difference determination. Accuracy of the area method depends on the improbable assumption that the volume or weight of detritus particles in the diet is directly proportional to particle area. Accuracy of the difference determination is limited by the accumulation of measurement errors associated with each component of the calculation. Direct estimation of volume with subsequent conversion to ash free dry weight requires the fewest assumptions and offers greatest accuracy. Precision was greatest for the volume estimation technique and lowest for the difference determination. Time required per sample can be substantially reduced by using a digitizer for measurement and a microcomputer for calculations.     

Estimation of Linear Regression Model with Random Coefficients Ensuring Almost Non-Negativity of Variance Estimators

For a linear regression model with random coefficients, this paper considers the estimation of the mean of coefficient vector which, in turn, involves the estimation of variances of random coefficients. The conventional estimation methods for it sometimes provides negative estimates. In order to circumvent this kind of difficulty, a proposal is forwarded and is examined in the light of existing ones.     

Numerical non-identifiability regions of the minimal model of glucose kinetics: superiority of Bayesian estimation

The so-called minimal model (MM) of glucose kinetics is widely employed to estimate insulin sensitivity (S(I)) both in clinical and epidemiological studies. Usually, MM is numerically identified by resorting to Fisherian parameter estimation techniques, such as maximum likelihood (ML). However, unsatisfactory parameter estimates are sometimes obtained, e.g. S(I) estimates virtually zero or unrealistically high and affected by very large uncertainty, making the practical use of MM difficult. The first result of this paper concerns the mathematical demonstration that these estimation difficulties are inherent to MM structure which can expose S(I) estimation to the risk of numerical non-identifiability. The second result is based on simulation studies and shows that Bayesian parameter estimation techniques are less sensitive, in terms of both accuracy and precision, than the Fisherian ones with respect to these difficulties. In conclusion, Bayesian parameter estimation can successfully deal with difficulties of MM identification inherently due to its structure.     

Estimation of median regression from measured values

The median regression function is defined and demonstrated by examples. A lemma with sufficient conditions for continuity and differentiation of the median regression function is proved. Its estimation from a random sample is deduced firstly based on the empirical distribution function and secondly based on a kernel estimation with Gaussian kernels. Both estimations are demonstrated by GALTONS historical example. A comparison between the empirical median regression function and the empirical regression of the first kind is made by an example. A hint for curve fitting with the median estimation equation is also given by an example.     

Roentgenogrammetry and roentgenodensitometry in assessment of several morphologic changes]

The work deals with the significance of quantitative rentgenological methods in morphological studies. The results of these methods used for estimation of certain developmental changes in the skeleton as well as for estimation of experimental data on the reparation of the bone tissue are analyzed. It is also shown that the quantitative rentgenological estimation of the state of soft tissues is possible. The role of rentgenogrammetry and rentgendensitometry in the intravital characteristics of the morphological changes associated with different factors is stressed.     

中国汉族女性长骨碎片的身高推断

本文作者通过对69副有生前确切资料的中国汉族女性四肢长骨的研究,在女性四肢长骨推断身高的基础上,参考国内外学者的研究方法,选择左右侧肱骨,尺骨,桡骨,股骨,胫骨,腓骨的局部测量项目,其中肱骨8项,尺骨3项,桡骨3项,股骨14项,胫骨8项,腓骨3项,用SPSS软件包对测量项目进行统计分析,建立了中国汉族女性破碎长骨推断身高的回归方程。本文所建方程可以用于中国汉族女性破碎长骨的身高推断,其中肱骨,股骨,胫骨碎片推断身高的效果较好。     

混合家系遗传参数估计方法研究

针对混合家系遗传参数估计,本文在假定公畜方差组分和母畜方差组分相等这一理论基础上,通过对方差分析的期望均方组成分析,提出了新的遗传力估计方法,以及某些特殊情况下的近似估计方法。通过一个估测实例比较了几种遗传力估计方法,结果表明,本文方法与全同胞组分估计最为接近,而且遗传力标准误最小,本文近似估计方法的效果也较好。对各种方法而言,资料越不平衡其差异越大。本文方法可以在一定程度上弥补全同胞分析时,因实际资料的公母畜方差组分差异过大的缺陷,具有实际可行性。此外,由于本文方法是用单因方差分析解决二因方差分析问题,计算更为简便,并可免于计算混合家系平均亲缘相关系数。     

Estimation of Error Rates by Means of Simulated Bootstrap Distributions

The bootstrap error estimation method is investigated in comparison with the known π-method and with a combined error estimation suggested by us using simulated and normally distributed “populations” in 15 and 30 characters, respectively. For small sample sizes (below the double to threefold number of characters per class) the estimates resulting from the bootstrap method are on the average too small and can no longer be accepted. Significantly better results (with an essentially lower calculation expenditure) are obtained for the π-method and the combined estimation. The variability is essentially the same for all the three methods. This applies both in the case of rather badly separated and in the case of very well separated populations. A bootstrap estimation modified by us also gives unsatisfactory results.     

Equivariant Estimation for the Parameters of an Exponential Model Based on Censored Sampling

Lehmann (1983) provides a detailed discussion on equivariant estimation of the parameters of location, scale and location-scale models. Edwin Prabakaran and Chandrasekar (1994) developed simultaneous equivariant estimation approach and illustrated the method with examples. In this paper, we consider exponential models and obtain minimum risk equivariant estimators of the parameters based on Type II censored samples. The equivariant method of estimation is illustrated with biological and reliability applications.     

模拟计算平行样对流域生物信息流估算的影响

流域生物信息流是流域生态学研究中的重要内容,是流域生态系统中的物质输移和能量输移过程的信息标记,是用eDNA技术调查评估河流水体中物种组成空间特征的基础。估算流域生物信息流是流域生态系统过程研究和eDNA技术调查评估河流水体中物种组成空间特征的关键。在有限的调查采样中,平行样的数量如何影响流域生物信息流的估算,尚待解答。基于随机抽样调查的基本原理,提出假设--采样数量不影响流域生物信息流估算结果的准确度,但会影响其精密度,然后通过问题简化转化和模拟计算,对该假设进行了检验。模拟计算结果显示,随着样点生物信息检出度(平行样数量)的增大,流域生物信息流估算结果会从偏小逐渐靠近流域生物信息流实际值,同时其99.9%置信区间也逐渐集中于流域生物信息流实际值。即样点生物信息检出度(平行样数量)对流域生物信息流估算的准确度和精密度均有影响。在实际调查研究过程中,建议先在所研究区域对平行样数量和样点生物信息检出度的关系进行预评估,然后基于流域生物信息流估算可信度目标在正式实施方案中经济有效地设置平行样,基于多平行样调查结果估算流域生物信息流,再根据各样点生物信息检出状况对流域生物信息流估算结果进行后验评估。     

Is torso soft tissue motion really an artefact within breast biomechanics research?

For rigid body POSE estimation, any relative movement of the tracking markers on a segment is often referred to as an artefact; however this may be an important part of the signal within breast biomechanics. This study aimed to quantify differences in breast range of motion when calculated relative to the torso segment using either direct or segment optimised POSE estimation algorithms. Markers on the torso and right nipple were tracked using infrared cameras (200 Hz) during five running gait cycles in three breast support conditions (no bra, everyday bra and sports bra). Multiplanar breast range of motion was calculated relative to the torso segment using two POSE estimation algorithms. First, the torso segment was defined using direct POSE estimation (direct). Second, while standing stationary in the anatomical position; the positional data of the torso markers were used to construct the torso using segment optimised POSE estimation (optimised). The torso segment length defined using direct POSE estimation changed significantly by 3.4 cm compared to that of the segment optimisation POSE estimation in the no bra condition. Subsequently, superioinferior breast range of motion was significantly greater (p<0.017) when calculated using direct POSE estimation, within each of the three breast support conditions. Segment optimisation POSE estimation is recommended to minimise any differences in breast motion associated with intra segment deformation between physical activity types. However, either algorithm is recommended when evaluating different breast support garments, as a correctly fitted bra does not cause the torso markers to move relative to each other.     

Comparing Simultaneous and Pointwise Confidence Intervals for Hydrological Processes

Distribution function estimation of the random variable of river flow is an important problem in hydrology. This issue is directly related to quantile estimation, and consequently to return level prediction. The estimation process can be complemented with the construction of confidence intervals (CIs) to perform a probabilistic assessment of the different variables and/or estimated functions. In this work, several methods for constructing CIs using bootstrap techniques, and parametric and nonparametric procedures in the estimation process are studied and compared. In the case that the target is the joint estimation of a vector of values, some new corrections to obtain joint coverage probabilities closer to the corresponding nominal values are also presented. A comprehensive simulation study compares the different approaches, and the application of the different procedures to real data sets from four rivers in the United States and one in Spain complete the paper.     

Estimation of Population Regression Coefficient in Successive Sampling

An attempt has been made to derive the theory of successive sampling for estimation of regression coefficient. The situations considered are estimation of regression coefficient for current occasion, estimation of change of regression coefficients over two occasions and estimation of average of regression coefficients over two occasions. The expressions of optimum estimators along with their variances have been worked out. On comparing their efficiencies empirically, it has been observed that similar to the estimation of mean, successive sampling can also be used with advantage for estimation of regression coefficient.     

Efficiency of haplotype frequency estimation when nuclear family information is included

In genetic studies the haplotype structure of the regarded population is expected to carry important information. Experimental methods to derive haplotypes, however, are expensive and none of them has yet become standard methodology. On the other hand, maximum likelihood haplotype estimation from unphased individual genotypes may incur inaccuracies. We therefore investigated the relative efficiency of haplotype frequency estimation when nuclear family information is included compared to estimation from experimentally derived haplotypes. Efficiency was measured in terms of variance ratios of the estimates. The variances were derived from the binomial distribution for experimentally derived haplotypes, and from the Fisher information matrix corresponding to the general likelihood function of the haplotype frequency parameters, including family information. We subsequently compared these variance ratios to the variance ratios for the case of estimation from individual genotypes. We found that the information gained from a single child compensates missing phase information to a high degree, resulting in estimates almost as reliable as those derived from observed haplotypes. Thus, if children have already been genotyped for other reasons, it is highly recommendable to include them into the estimation. If child information is not already present, it depends on the number of loci and the haplotype diversity if it is useful to genotype a single child just to reduce phase ambiguity. In general, if the number of loci is less than or equal to three or if the number of haplotypes with a frequency >5% is less than or equal to four, haplotype estimation from individuals is quite good already and the improvement gained from a single child can not compensate the genotyping effort for it. On the other hand, under scenarios with many loci and high haplotype diversity, haplotype frequency estimation from trios can be more efficient than haplotype frequency estimation from individuals also on a per genotype base.     

State estimation of a Uricase production process with Candida Utilis

The present work discusses the implementation of a Kalman filtering procedure in a state estimation of a batch Uricase production process with Candida Utilis. An unstructured model of the process is used for the estimation procedure. The observability is thoroughly investigated and a Kalman filter is applied afterwards as a powerful and precise state estimation tool. The estimates in all cases of observability are presented, compared and discussed.     

Sample Size Reassessment in Adaptive Clinical Trials Using a Bias Corrected Estimate

Point estimation in group sequential and adaptive trials is an important issue in analysing a clinical trial. Most literature in this area is only concerned with estimation after completion of a trial. Since adaptive designs allow reassessment of sample size during the trial, reliable point estimation of the true effect when continuing the trial is additionally needed. We present a bias adjusted estimator which allows a more exact sample size determination based on the conditional power principle than the naive sample mean does.