Temporal relationship between left ventricular and arterial systemelastances

Arterial compliance is an important component of ventricular afterload. Although its pressure dependence has been recognized, its temporal relationship to ventricular elastance (Elv(t)) has not been established. We investigated this in five open chest anesthetized dogs where simultaneous aortic pressure and flow and left ventricular pressure were measured. Elv(t) was derived using an elastance-resistance model of the left ventricle assuming an ejection fraction of 0.50 and a dead volume (Vd) of 3.0 mL. The nonlinear pressure-dependent compliance (C(P)) of the arterial system was incorporated in a three-element Windkessel model and determined by accurate prediction of aortic pressure from aortic flow. The resulting arterial elastance (Eas(t)) was computed as Eas(t) = 1/C(P). Results show that Eas(t) reaches a minimum value at or near the start of ventricular ejection and attains its peak value at or near the same time maximum LV elastance (Emax) is reached, at end-systole. Finally, numerical simulation of the model demonstrates its ability to adequately reproduce measured pressure and flow. Thus, the arterial system, in terms of elastance, is dynamically and temporally coupled to the left-ventricle during ejection.     

Correlation function of quantized sine wave plus Gaussian noise

The output correlation function is derived for anN-step, symmetric amplitude quantizer when the input is the sum of a sine wave and stationary, zero mean, Gaussian noise. The derivation, which follows the transform method of Rice, Bennett, and Middleton, is perfectly general in that there are no restrictions on the quantizer characteristic or the signal or noise powers. Results of previous studies are restricted to quantizers with uniform steps or to cases in which the rms amplitude of the input is small compared to the amplitude range covered by the quantizer. For low signal-to-noise ratios, approximations can be made which greatly simplify the calculations. These approximations are described, and graphical results are presented for two, three, and four bit linear quantizers when the input noise spectrum is rectangular narrow-band and the SNR is small.     

代谢综合征与肱-踝脉搏波传导速度的相关性研究

研究了惯性测量组件和GPS测姿组件安装偏差关系的标定算法,利用双差载波相位测量内蕴的矢量转换关系对安装偏差矩阵进行估计.首先,分析了双差载波相位观测方程,将其转换为已知GPS基线矢量和对应双差载波相位观测在惯性测量组件载体系中的投影,将安装偏差矩阵表示为矢量方程参数;其次,根据矢量方程特性,将其归结为Wahba问题,并利用QUEST方法对耦合噪声条件下的安装偏差矩进行最优估计阵;最后利用仿真数据和实验数据验证了标定算法,并与其他方法进行了比较.分析表明,标定算法在精度和鲁棒性上都得到了提高,有很强的实用性.     

超声对糖尿病患者颈动脉改变及左心室功能改变关系的研究

目的:探讨糖尿病患者颈动脉改变与左心室功能改变的关系.方法:对215例糖尿病患者和178例健康人行颈动脉及左心室超声检查分析.结果:糖尿病组215例患者中颈动脉内膜与中层(intima-media thickens,IMT)增厚者为203例,占94.4%,粥样硬化斑块形成146例,共312处,IMT值(P<0.05)及...     

Correlation of esophageal conductance measurements with aortic and left ventricular diameters and stroke volume

Esophageal conductance measurements were correlated with hemodynamic events in 9 dogs chronically instrumented for measurement of left ventricular (LV) and aortic pressures, LV short axis and descending aortic diameters, and aortic blood flow. A four-electrode conductance catheter was positioned in the esophagus. Both an internal and an internal/external configuration were examined during anesthesia with hemodilution, pulmonary lavage and dobutamine infusion. LV stroke volume was altered by caval occlusion at each intervention. Stroke conductance was highly correlated to aortic or LV diameters and stroke volume over a range of diameters depending on the electrode configuration. Esophageal conductance measurements are directly influenced by local hemodynamic events adjacent to the site of measurement.     

Discrimination between healthy and diseased hearts by spectra decomposition of their left ventricular three-dimensional geometry

A quantitative procedure for 3-D shape analysis of human LVs (left ventricles,) studied in vivo by 3-D computed tomography is introduced. The procedure utilizes the geometrical cardiogram signal for characterizing the 3-D shape of the studied LV, and uses spectral decomposition to obtain the corresponding feature vectors needed for automatic classification. It was found that the different pathological states of the LV were associated with characteristic changes in the geometrical spectrum domain. Representing each heart by a feature vector in the spectral domain and applying unsupervised fuzzy clustering of the obtained 27 feature vectors, an overall success of 85% in classification was obtained. These results indicate that an operator-independent shape-based diagnosis is potentially feasible for the four different pathological categories studied.     

Extraction and quantification of left ventricular deformation modes

We have developed a method that decomposes the deformation of the left ventricle (LV) between end diastole (ED) and end systole (ES) into separate deformation modes such as longitudinal shortening, wall thickening, and twisting. The deformation was initially found from the motion of an LV finite-element mesh that was fitted to clinically obtained magnetic resonance (MR) tagged images. A mode coefficient was calculated for each deformation mode to quantify the different modes and, thus allowing for discrimination of normal and abnormal deformation patterns. We applied the method to 13 normal subjects and 13 diabetes patients. By using the ED mesh as reference and adding the extracted deformation modes multiplied by their mode coefficients, an approximate ES mesh was calculated and compared with the "true" ES mesh found from the MR images. For the 26 subjects the average Euclidean distance was less than 1.7+/-0.9 mm between the nodes of the approximated and true ES meshes. The coefficient values for the patient group showed significantly less longitudinal shortening, less wall thickening, more longitudinal twisting and also more bulging of the septum into the LV when compared with the normal subjects. We conclude that the developed method successfully quantifies the deformation into several modes of deformation and is capable of distinguishing the deformation of a group of patients from a group of normal subjects.     

Support vector analysis of color-Doppler images: a new approach for estimating indices of left ventricular function

Reliable noninvasive estimators of global left ventricular (LV) chamber function remain unavailable. We have previously demonstrated a potential relationship between color-Doppler M-mode (CDMM) images and two basic indices of LV function: peak-systolic elastance (Emax) and the time-constant of LV relaxation (tau). Thus, we hypothesized that these two indices could be estimated noninvasively by adequate postprocessing of CDMM recordings. A semiparametric regression (SR) version of support vector machine (SVM) is here proposed for building a blind model, capable of analyzing CDMM images automatically, as well as complementary clinical information. Simultaneous invasive and Doppler tracings were obtained in nine mini-pigs in a high-fidelity experimental setup. The model was developed using a test and validation leave-one-out design. Reasonably acceptable prediction accuracy was obtained for both Emax (intraclass correlation coefficient Ric, = 0.81) and tau (Ric, = 0.61). For the first time, a quantitative, noninvasive estimation of cardiovascular indices is addressed by processing Doppler-echocardiography recordings using a learning-from-samples method.     

Detecting left ventricular endocardial and epicardial boundaries by digital two-dimensional echocardiography

Cardiac function is evaluated using echocardiographic analysis of shape attributes, such as the heart wall thickness or the shape change of the heart wall boundaries. This requires that the complete boundaries of the heart wall be detected from a sequence of two-dimensional ultrasonic images of the heart. The image segmentation process is made difficult since these images are plagued by poor intensity contrast and dropouts caused by the intrinsic limitations of the image formation process. Current studies often require having trained operators manually trace the heart walls. A review of previous work is presented, along with how this problem can be viewed in the context of the computer vision area. A novel algorithm is presented for detecting the boundaries. This algorithm first detects spatially significant features based on the measurement of image intensity variations. Since the detection step suffers from false alarms and missing boundary points, further processing uses high-level knowledge about the heart wall to label the detected features for noise rejection and to fill in the missing points by interpolation.