二尖瓣狭窄下游湍流剪应力体内多普勒超声定量检测的方法学研究

为建立与计算机图像分析技术相结合的人体二尖瓣狭窄下游湍流剪应力 (Turbulent shear stree,TSS)多普勒超声定量检测方法。本研究采用多普勒超声技术对正常人和不同程度二尖瓣狭窄病人组的二尖瓣下游血流速度频谱等指标进行多位点记录 ,通过计算机图像分析技术 ,测算出相关位点 TSS大小。结果表明不同程度二尖瓣狭窄组的二尖瓣下游核心位点 TSS与相对扰动强度 (Relative turbulent intensity,Irel)始终低于边界各位点 ;边界位点 TSS、Irel和流场均匀性等指标在正常对照和不同程度狭窄组之间均存在显著性差异 (P<0 .0 5 ) ,并均与有效瓣口面积 (Effective orifice area,EOA)明显相关。结论为二尖瓣有效开口面积越小 ,狭窄越重 ,射流边界 TSS和 Irel越大 ,射流核心区 TSS明显低于边界。研究结果显示 :应用与计算机图像分析相结合的多普勒超声技术所测人体二尖瓣狭窄下游 TSS与流场均匀性等常规多普勒超声指标具有良好的相关性 ,能共同反映不同狭窄程度病人瓣区血流动力学变化 ,因此表明本方法能比较准确地刻画人体二尖瓣狭窄下游 TSS的大小 ,并且 ,还具有安全无创、抗干扰能力强的特点 ,可用于瓣膜病病人瓣区流场中 TSS的定量分析。     

Asymmetry of Doppler spectrum in stenosis differentiation

The asymmetry of the spectral distribution of ultrasonic Doppler flow velocity signals, assessed using the coefficient of skewness, is discussed as a criterion of stenosis differentiation. Its performance is compared with that of the index of turbulence intensity for both in vitro and in vivo flow Doppler signals, recorded distal to a stenosis. The power spectral distributions are computed using the direct Fourier transform and maximum likelihood method. The asymmetry of spectral distribution has proved to be a more efficient criterion than the turbulence intensity. The maximum likelihood method ensures better stenosis differentiation than the direct FFT method.     

Comparison of FFT and adaptive ARMA methods in transcranial Doppler signals recorded from the cerebral vessels

In this work, transcranial Doppler signals recorded from the temporal region of the brain on 35 patients were transferred to a personal computer by using a 16-bit sound card. Fast Fourier transform and adaptive auto regressive-moving average (A-ARMA) methods were applied to transcranial Doppler frequencies obtained from the middle cerebral artery in the temporal region. Spectral analyses were obtained to compare both methods for medical diagnoses. The sonograms obtained using A-ARMA method give better results for spectral resolution than the FFT method. The sonograms of A-ARMA method offer net envelope and better imaging, so that the determination of blood flow and brain pressure can be calculated more accurately. All diseases show higher resistance to flow than controls with no difference between males and females. Whereas values between disease classes differed, resistance within each class was remarkably constant.     

Pattern and clinical aspects of congenital heart diseases and their management in Cameroon

The aim of the study was to investigate the occurrence, pattern and clinical aspects of congenital heart diseases and their management in Cameroon. In this multicentred retrospective study from January 2006 till November 2009, out of 51,636 consulting in three referral centres, 505 were recruited. All the patients presented with the following symptoms: cyanosis, clubbing, frequent respiratory tract infections, failure to thrive, growth retardation, precordial murmur and dyspnoea. Patients were sent for the screening of congenital heart disease. After the comprehensive Doppler echocardiography, the recruited patients were diagnosed with congenital (67.2%) and in few, acquired heart disease. Heart murmur, dyspnoea and growth retardation was the triad mostly encountered. The occurrence of congenital heart diseases in Cameroon is 9.87%. In Douala, isolated ventricular septal defect, interatrial septal defect and isolated pulmonary valve stenosis were more diagnosed than in Shisong (P <0.05) and Yaoundé (P <0.05). In Yaoundé, there were more cases of common arterial trunk, transposition of great arteries with ventricular septal defect and Ebstein disease than in Shisong (P <0.05) and Douala (P < 0.05). At Shisong, tetralogy of Fallot, arterial duct, coarctation of the aorta, congenital mitral valve regurgitation, atresia of the tricuspid valve, double outlet right ventricle, anomalous pulmonary venous return and left isomerism were more diagnosed than in Yaoundé (P <0.05) and in Douala (P <0.05). Thirty percent of the patients were operated abroad; 9% in the cardiac centre. Conclusion: Our data show that congenital heart diseases are represented in Cameroon as in the literature; isolated ventricular septal defect is the predominant pathology.     

Value of a modified continuity equation method to quantify mitral valve area in patients with mitral stenosis and sinus rhythm

Summary To quantify valve area in mitral stenosis, a modified continuity equation method using continuous wave Doppler and thermodilution measurements was applied. In 14 patients with mitral stenosis and sinus rhythm (age: 49±11 years), transmitral flow velocity was recorded by continuous wave Doppler during right and left heart catheterization. Mitral valve area was calculated by three different methods: 1. According to the continuity equation, stroke volume (thermodilution technique) was divided by the registered time velocity integral of the mitral stenotic jet (continuous wave Doppler). 2. Mitral valve area was calculated by the pressure half-time method. 3. Simultaneous pulmonary capillary wedge and left ventricular pressure measurements were used for determination of mitral valve area according to the Gorlin formula. The mitral valve area determined by application of the continuity equation (y) showed a close correlation to the valve area calculated by the Gorlin equation (x):y=0.73x+0.12, SEE=0.11 cm²,r=0.88,P<0.001. In contrast, the correlation between mitral valve area determined by pressure half-time (y) and the Gorlin formula (x) was not as good:y=0.77x+0.11, SEE=0.26 cm²,r=0.65,P<0.05. Thus, the continuity equation method using combined continuous wave Doppler and thermodilution technique allows a valid determination of mitral valve area. In patients with mitral stenosis and sinus rhythm, this technique is superior to the noninvasive determination of mitral valve area by the conventional pressure half-time method.Abbreviations bpm beats per minute - CO cardiac output - DFT diastolic filling time - HR heart rate - LV left ventricular - MMVG mean mitral valve gradient - MVA_CE mitral valve area determined according to the continuity equation method - MVA_G mitral valve area calculated according to the Gorlin formula - MVA_T1/2 mitral valve area determined according to the pressure half-time method - PCW pulmonary capillary wedge - SV stroke volume     

Spectral analysis of internal carotid arterial Doppler signals using FFT, AR, MA, and ARMA methods

In this study, Doppler signals recorded from internal carotid artery of 45 subjects were processed by PC-computer using classical (fast Fourier transform) and model-based (autoregressive, moving average, autoregressive moving average (ARMA) methods) methods. Power spectral density estimates of internal carotid arterial Doppler signals were obtained using these spectral analysis methods. The variations in the shape of the Doppler power spectra as a function of time were presented in the form of sonograms in order to determine the degree of internal carotid artery stenosis. These Doppler power spectra and sonograms were then used to compare the applied methods in terms of their frequency resolution and the impact on determining stenosis in internal carotid arteries. Based on the results, performance characteristics of the autoregressive and ARMA methods were found extremely valuable for spectral analysis of internal carotid arterial Doppler signals obtained from healthy subjects and unhealthy subjects having artery stenosis.     

Determination of aorta failure with the application of FFT, AR and wavelet methods to Doppler technique

In this study, Doppler signals recorded from the output of aorta valve of 24 patients were transferred to a personal computer (PC) by using a 16-bit sound card. Doppler difference frequencies were recorded from each of the patients, and then analyzed using fast Fourier transform, autoregressive and wavelet transform analyzer to obtain their sonograms. These sonograms are then used to compare with the applied methods in terms of medical evaluation.     

Comparison of eigenvector methods with classical and model-based methods in analysis of internal carotid arterial Doppler signals

Doppler ultrasound is known as a reliable technique, which demonstrates the flow characteristics and resistance of arteries in various vascular disease. In this study, internal carotid arterial Doppler signals recorded from 105 subjects were processed by PC-computer using classical, model-based, and eigenvector methods. The classical method (fast Fourier transform), two model-based methods (Burg autoregressive, least-squares modified Yule-Walker autoregressive moving average methods), and three eigenvector methods (Pisarenko, multiple signal classification, and Minimum-Norm methods) were selected for processing internal carotid arterial Doppler signals. Doppler power spectra of internal carotid arterial Doppler signals were obtained using these spectrum analysis techniques. The variations in the shape of the Doppler power spectra were examined in order to obtain medical information. These power spectra were then used to compare the applied methods in terms of their frequency resolution and the effects in determination of stenosis and occlusion in internal carotid arteries.     

Diagnosing Aortic Valve Stenosis by Parameter Extraction of Heart Sound Signals

The objective of this study was to develop an automatic signal analysis system for heart sound diagnosis. This should support the general practitioner in discovering aortic valve stenoses at an early stage to avoid or decrease the number of surgical interventions. The applied analysis method is based on classification of heart sound signals utilising parameter extraction. From the wavelet decomposition of a representative heart cycle as well as from the Short Time Fourier Transform (STFT) and the Wavelet Transform (WT) spectra new time series were derived. In several segments, parameters were extracted and analysed. In addition, features of the Fast Fourier Transform (FFT) of the raw signal were examined. In this study, 206 patients were enrolled, 159 with no heart valve disease or any other heart valve disease but aortic valve stenosis and 47 suffering from aortic valve stenosis in a mild, moderate or severe stage. To separate the groups, a linear discriminant function analysis was applied leading to a reduced parameter set. The introduced two classification stage (CS) system for automatic detection of aortic valve stenoses achieves a high sensitivity of 100% for moderate and severe aortic valve stenosis and a sensitivity of 75% for mild aortic valve stenosis. A specificity of 93.7% for patients without aortic valve stenosis is provided. The developed method is robust, cost effective and easy to use, and could, therefore, be a suitable method to diagnose aortic valve stenosis by general practitioners.     

A comparison of different feature extraction methods for diagnosis of valvular heart diseases using PCG signals

This article presents a novel method for diagnosis of valvular heart disease (VHD) based on phonocardiography (PCG) signals. Application of the pattern classification and feature selection and reduction methods in analysing normal and pathological heart sound was investigated. After signal preprocessing using independent component analysis (ICA), 32 features are extracted. Those include carefully selected linear and nonlinear time domain, wavelet and entropy features. By examining different feature selection and feature reduction methods such as principal component analysis (PCA), genetic algorithms (GA), genetic programming (GP) and generalized discriminant analysis (GDA), the four most informative features are extracted. Furthermore, support vector machines (SVM) and neural network classifiers are compared for diagnosis of pathological heart sounds. Three valvular heart diseases are considered: aortic stenosis (AS), mitral stenosis (MS) and mitral regurgitation (MR). An overall accuracy of 99.47% was achieved by proposed algorithm.     

Analysis of the first heart sound using the matching pursuit method

It is acknowledged that the first heart sound S1 consists of two major, high-frequency components M1 and T1, corresponding, respectively, to the vibrations of the mitral and tricuspid valves and their surrounding tissues following valve closure in early systole. In this study, the matching pursuit (MP) method was used to decompose S1 into a series of time-frequency atoms. M1 and T1 were separated from the parameterised atoms of S1. The first two dominant frequencies of M1 were identified and used as features of a linear classifier to diagnose mitral valve abnormality. This method was applied to two sets of S1 data recorded from 15 patients with normal, and 15 patients with abnormal, bioprosthetic mitral valves, respectively. It was found that the two features exhibit significant differences between the normal and abnormal sets (p< 0.001). Using these two features, a correct classification of 93% was obtained. In addition, when the Wigner distribution of S1 was calculated from the decomposed atoms and compared with a spectrogram, the MP method provided better results. The study demonstrates that the MP method may be a promising technique for heart sound analysis.     

Wavelet-based neural network analysis of ophthalmic artery Doppler signals

In this study, ophthalmic artery Doppler signals were recorded from 115 subjects, 52 of whom had ophthalmic artery stenosis while the rest were healthy controls. Results were classified using a wavelet-based neural network. The wavelet-based neural network model, employing the multilayer perceptron, was used for analysis of ophthalmic artery Doppler signals. A multilayer perceptron neural network (MLPNN) trained with the Levenberg-Marquardt algorithm was used to detect stenosis in ophthalmic arteries. In order to determine the MLPNN inputs, spectral analysis of ophthalmic artery Doppler signals was performed using wavelet transform. The MLPNN was trained, cross validated, and tested with training, cross validation, and testing sets, respectively. All data sets were obtained from ophthalmic arteries of healthy subjects and subjects suffering from ophthalmic artery stenosis. The correct classification rate was 97.22% for healthy subjects, and 96.77% for subjects having ophthalmic artery stenosis. The classification results showed that the MLPNN trained with the Levenberg-Marquardt algorithm was effective to detect ophthalmic artery stenosis.     

Classification of carotid artery stenosis of patients with diabetes by neural network and logistic regression

The blood flow hemodynamics of carotid arteries were obtained from carotid arteries of 168 individuals with diabetes using the 7.5 MHz ultrasound Doppler M-unit. Fast Fourier Transform (FFT) methods were used for feature extraction from the Doppler signals on the time-frequency domain. The parameters, obtained from the Doppler sonograms, were applied to the mathematical models that were constituted to analyze the effect of diabetes on internal carotid artery (ICA) stenosis. In this study, two different mathematical models such as the traditional statistical method based on logistic regression and a Multi-Layer Perceptron (MLP) neural network were used to classify the Doppler parameters. The correct classification of these data was performed by an expert radiologist using angiograpy before they were executed by logistic regression and MLP neural networks. We classified the carotid artery stenosis into two categories such as non-stenosis and stenosis and we achieved similar results (correctly classified (CC) = 92.8%) in both mathematical models. But, as the degree of stenosis had been increased to 4 (0-39%, 40-59%, 60-79% and 80-99% diameter stenosis), it was found that the neural network (CC = 73.9%) became more efficient than the logistic regression analysis (CC = 67.7%). These outcomes indicate that the Doppler sonograms taken from the carotid arteries may be classified successfully by neural network.     

Potential of endovascular surgery in children with congenital heart diseases

Bakulev Scientific Center for Cardiovascular Surgery has very broad experience (3,774 cases) in endovascular operations on patients with different congenital heart diseases. 648 patients have undergone atrioseptostomy, 1,500 patients--balloon valvuloplastic, 546 patients--balloon angioplasty, 565 patients--coil embolization; stents have been used in 86 patients; in 114 cases the surgeons of the Center have used different occluders to close pathological communications, in 315 patients others types of treatment have been applied. In many cases of congenital heart diseases (valvular stenosis of pulmonary artery and aorta, patent ductus arteriosus, atrium and ventricular septal defect, coronary-cardial fistula, coarctation and recoarctation of aorta) endovascular techniques present a good alternative to open surgical procedures. In some cases endovascular techniques are palliative (Rashcind's and Park's procedure, balloon valvuloplasty in stenosis of pulmonary artery and angioplasty in stenosis of peripheral pulmonary artery and aorto-pulmonary anastomosis in patient with cyanotic congenital heart disease, stenting of PDA, coil embolization of collateral artery from aorta to pulmonary artery etc.) Multi-stage endovascular and surgical treatment allows radical or hemodynamic correction in patient with complex congenital heart disease. Nowadays the spectrum of endovascular treatment is wide; it is going to develop in the future, which will allow endovascular techniques to be of wide use in treatment of patient with complex heart diseases.     

左心室压力-容积关系的仿真

目的　仿真前、后负荷改变及各种瓣膜病情况下 ,左心室压力 容积关系的改变。方法　应用Ursino所建立的全循环系统集中参数模型 ,对跨瓣流动方程作了适当修改。结果　首先 ,讨论了心室前负荷、后负荷和心肌收缩力对左心室压力 容积关系的影响 ;在此基础上仿真了二尖瓣狭窄、二尖瓣关闭不全 ,主动脉瓣狭窄和主动脉瓣双病变的左心室压力 容积环。结论　仿真结果显示了心脏瓣膜疾病对左心室功能的影响 ,同时说明集中参数模型不但能描述血管系统 ,也能较好地模拟心脏功能     

Quantification of mitral regurgitation using Doppler echocardiography

Examining 18 patients without regurgitation (control group) and 20 patients with mitral regurgitation the method of Doppler echokardiography, the pulsating and continual wave, was applied. Both groups consisted of patients with heart diseases in whom a heart catheterization was indicated for the evaluation of the actual state. Thus, the competence of the mitral orifice was also evaluated. The aim of the investigation was to quantify or more accurately estimate the degree of mitral regurgitation. The accuracy of the Doppler method was tested by means of the measured cardiac stroke volumes of the right heart side in both groups with the probability higher than 80% but lower than 90% (0.80 less than p 0.23 less than 0.90), and by testing the cardiac stroke volumes of the right heart and the diastolic volumes of the left heart (control group: mitral and pulmonal orifice) with the probability higher than 70% and lower than 80% with the error of 6% of the measured volume. The regurgitation mitral fraction (RF) was presented as the difference between the total forwards volume (TFV) in the mitral orifice and the net forwards volume (NFV) in the pulmonal orifice and calculated in cm3, and as the regurgitation fraction index (RFI) in percentages (RFI = 1--NFV/TFV). It was also expressed as the regurgitation volume index (RVI) whose values were divided into degrees, according to the literature data. Numerical values of the regurgitation fraction and the regurgitation fraction index were classified in the mitral insufficiency degrees from 1 to 4. A fast regurgitation screening was possible due to the regurgitation fraction index, which is lower than the 45% of the diastolic volume in the cases of mild mitral insufficiency, while the values over 45% denote severe mitral insufficiency. The obtained values of the regurgitation fraction and index divided into degrees showed a close correlation with the angioventriculographic degrees obtained by heart catheterization. Cardiac output measured by the Doppler and Fick methods in the group with regurgitation showed a high correlation coefficient (r = 0.85) (0.60 less than p 0.47 less than 0.70).     

多普勒超声检测二尖瓣流场均匀性的方法学研究

为了建立体内天然二尖瓣流均匀性的多普勒超声定量检测方法，采用先进的彩色多普勒血流图像技术对正常人及二尖瓣狭窄患者二尖瓣下流场均匀性进行对比研究。发现流场均匀性定量指标在正常人及二尖瓣狭窄患者之间有显著差异，该指标与常规多普勒超声指标明显相关。该方法具有安全无创、准确可靠及可重复性强等优点，能台实地反映正常人及二尖瓣狭窄患者二尖瓣下游流场均匀性的优劣状况。     

基于功率谱信息熵的异常心音识别

目的：异常心音识别是心血管疾病检测的一种重要手段,为了探究异常心音频域的有用信息,提出了将不同频段的功率谱作为一个独立信源计算其信息熵,从而对房室瓣和动脉瓣异常信号进行判别的一种新方法。方法：实验先将心音信号进行小波包分解,然后利用改进的Welch方法计算信号的功率谱,进而求各频段的功率谱信息熵,再建立支持向量机预测模型来对两种异常心音进行识别。结果：选取二尖瓣狭窄、二尖瓣关闭不全、主动脉瓣狭窄、主动脉瓣关闭不全共27例心音信号进行算法仿真,其中房室瓣异常能够全部检测出来,动脉异常有3例被误判,正确率达到77％;在原有27例信号的基础上,增加3例房室瓣异常和3例动脉异常信号进行算法验证,房室瓣异常信号仍然能够全部被检测出来．动脉异常信号2例被误判。结论：从仿真结果可以看出,该算法对房室瓣异常和动脉异常两种心音信号有较高的识别率。尤其对房室瓣杂音能够完全识别,也表明功率谱信息熵在异常心音的识别中具有重要意义。     

Cronic non-rheumatic valvular heart disease. An autopsy study.

The frequency of chronic non-rheumatic valvular heart disease in Iceland was investigated via autoposies performed from November 1965 through December 1974. During this period, about 12.400 Icelanders died at the age of 16 years and older and 28.8 per cent of these were included in the study. At autopsy, males outnumbered females by 2:1. The frequency of calcific aortic stenosis was found to be 3.63 per cent and the prevalence was calculated to be 3.17 per cent among males and 4.50 per cent among females. Calcific aortic stenosis in tricuspid valves was more frequent in females and calcific aortic stenosis in bicuspid valves was more frequent in males. Among the hearts with calcific aortic stenosis, 70.8 per cent were found to have normally tricuspid valves, 25.4 per cent bicuspid valves and 3.8 per cent tricuspid valves with an unicommissural fusion. In 0.59 per cent of the hearts the aortic valve was either bicuspid or had an uncommissural fusion without the features of calcific stenosis. However, a functional stenosis was suggested by the increased weight of most of these hearts. The frequency of bicuspid aortic valves was 1.2 per cent with a prevalence in males of 1.54 per cent and in females 0.50 per cent. A calcified mitral annulus was found in 1.98 per cent of the hearts and in most, it was either associated with calcific aortic stenosis in a tricuspid valve, or it was a single valvular disease. Rheumatic valvular disease was found in 1.08 per cent of the heart examined.     

Rate-adaptive pacemaker controlled by motion and respiratory rate using neuro-fuzzy algorithm

Rate-adaptive pacemakers use information from sensors to change the rate of heart stimulation. Until now, fuzzy-pacemaker algorithms have been used to combine inputs from sensors to improve heart rate control, but they have been difficult to implement. In this paper, a pacemaker algorithm which controlled heart rate adaptively by motion and respiratory rate was studied. After chronotropic assessment exercise protocol (CAEP) tests were performed to collect activity and respiratory rate signals, the intrinsic heart rate was inferred from these two signals by a neuro-fuzzy method. For 10 subjects the heart rate inference, using the neurofuzzy algorithm, gave 52.4% improved accuracy in comparison with the normal fuzzy table look-up method. The neuro-fuzzy method was applied to a real pacemaker by reduced mapping of the neuro-fuzzy look-up table.