Analysis of the four heart sounds statistical study and spectro-temporal characteristics

Abstract

Heart auscultation has been recognised for a long time as an important tool for the diagnosis of heart disease; it is the most common and widely recommended method to screen for structural abnormalities of the cardiovascular system. Detecting relevant characteristics and forming a diagnosis based on the sounds heard through a stethoscope, however, is a skill that can take years to be acquired and refine. The efficiency and accuracy of diagnosis based on heart sound auscultation can be improved considerably by using digital signal processing techniques to analyse phonocardiographic (PCG) signals. The study of the functioning of the heart is very important for the diagnosis of different cardiac pathologies. The phonocardiogram signal (PCG) is the signal generated after conversion of the sound noises coming from the heart into an electrical signal, it groups together a set of four cardiac noises (S1, S2, S3, S4) which are in direct correlation with cardiac activity. The short-term Fourier Transform (STFT) is an analytical technique that describes the evolution of the time and frequency behaviour of these four heart sounds. A statistical study has been carried out in this direction in order to better highlight the characteristics of the PCG signal. A fairly high number of cycles (twenty) was used to further refine the expected results. The objective of this paper is to use a statistical analysis based on the results obtained by the use of The STFT technic this in order to find statistical parameters (mean, standard deviation, etc.) which can give us a clear vision of the electrophysiological behaviour of the phonocardiogram signal. This aspect has not been done so far and which however can give appreciable practical results.     

FT-IR光谱法研究TA9902抑制Aβ1-42聚集的分子机制

目的：研究TA9902引起Aβ1-42二级结构变化，了解其抑制Aβ聚集和纤维形成的机制。 方法： 用傅里叶变换红外光谱法（FT-IR）曲线拟合定量分析和特征频谱分析Aβ老化后及TA9902干预后二级结构的变化。 结果： 1 700-1 600 cm-1之间的酰胺I带分析，Aβ1-42单独孵育30 min时，β-折叠片为46.53％；Aβ1-42老化72 h，β-折叠片为65.13％，β-折叠片增加了19.4％。TA9902与Aβ1-42作用β-折叠片为29.04％；与Aβ1-42老化72 h相比，TA9902的存在明显减少β折叠片的含量(下降了36.09％)，β-转角的含量明显升高(升高57.56％)，α-螺旋也略有升高（2.93％）。呈现出明显的β折叠片向β-转角的转化。其它基团特征峰位显示，TA9902中有酮类尤其是饱和链状酮或α-二酮和烷烃类的-CH2和-CH参与了Aβ1-42的分子变构。 结论： TA9902明显抑制Aβ1-42β-折叠的形成，同时也使Aβ1-42肽的侧链基团发生了化学修饰，提示Aβ的聚集和纤维形成与其分子β-折叠的形成和侧链基团发生了化学修饰有关。     

基于多分辨分析与连续小波变换提取和分析兔体感诱发电位

研究单次提取兔体感诱发电位,并定位和分析诱发电位波形成分。麻醉兔,以0.5Hz频率电脉冲刺激兔下肢隐神经,3764Hz采样率收集兔头皮电位。采用一维多分辨分析提取兔体感诱发电位,并用连续小波变换定位和分析诱发电位波形成分。单次诱发电位的小波变换与叠加平均诱发电位比较,表明Daubechies小波多分辨分析可以单次提取诱发电位。连续小波变换能够精确定位诱发电位中波形成分,并可采用连续小波变换分析诱发成分的频域特性。连续小波变换技术把一维时域信号投影到二维时频空间研究将成为医学信号处理的一个有用方法。     

A mixture of experts network structure for modelling Doppler ultrasound blood flow signals

Mixture of experts (ME) is a modular neural network architecture for supervised learning. This paper illustrates the use of ME network structure to guide modelling Doppler ultrasound blood flow signals. Expectation-Maximization (EM) algorithm was used for training the ME so that the learning process is decoupled in a manner that fits well with the modular structure. The ophthalmic and internal carotid arterial Doppler signals were decomposed into time-frequency representations using discrete wavelet transform and statistical features were calculated to depict their distribution. The ME network structures were implemented for diagnosis of ophthalmic and internal carotid arterial disorders using the statistical features as inputs. To improve diagnostic accuracy, the outputs of expert networks were combined by a gating network simultaneously trained in order to stochastically select the expert that is performing the best at solving the problem. The ME network structure achieved accuracy rates which were higher than that of the stand-alone neural network models.     

中性粒细胞分布数据序列小波变换研究

目的　研究小波分析结合复倒谱处理血膜中中性粒细胞分布数据信号的新方法及临床意义。方法　利用“虚拟盒计数法”方法采集血膜中的细胞分布数据信号 ,然后进行小波变换结合复倒谱分析方法处理。结果　提出血膜中细胞分布状态的“密集子”新概念。计算出小波变换复倒谱主极大值 ,并以此作为不同情况下血膜中中性粒细胞分布“密集子”间距的准确参数。结论　方法简单 ,能较好的反映血膜中细胞分布状态的微观组织结构特征 ,是血膜中细胞“密集子”表达定征的一种有效方法     

基于离散小波变换提取脑机接口中脑电特征

在脑机接口中，针对脑电特征提取利用单一种类信息、使用数据量大、分类性能较差等缺点，提出一种新颖的基于离散小波变换的方法。分析了小波变换特征提取的特点和特征表示方式，用Daubechies类db4小波函数对脑电信号进行6层分解，抽取小波变换各子带关键的部分逼近系数、小波系数、小波子带系数均值组成特征向量。以分类正确率为指标检验了提取特征的性能。实验结果表明，这种方法能够利用少量数据提取脑电信号本质特征，具有较高的分类性能，为利用脑电识别人的不同意图提供了快速而有效的手段。     

Wavelet transform analysis of heart rate variability during myocardial ischaemia

Analysis of heart rate variability (HRV) is a valuable, non-invasive method for quantifying autonomic cardiac control in humans. Frequency-domain analysis of HRV involving myocardial ischaemic episodes should take into account its non-stationary behaviour. The wavelet transform is an alternative tool for the analysis of non-stationary signals. Fourteen patients have been analysed, ranging from 40 to 64 years old and selected from the European Electrocardiographic ST-T Database (ESDB). These records contain 33 ST episodes, according to the notation of the ESDB, with durations of between 40s and 12min. A method for analysing HRV signals using the wavelet transform was applied to obtain a time-scale representation for very low-frequency (VLF), low-frequency (LF) and high-frequency (HF) bands using the orthogonal multiresolution pyramidal algorithm. The design and implementation using fast algorithms included a specially adapted decomposition quadrature mirror filter bank for the frequency bands of interest. Comparing a normality zone against the ischaemic episode in the same record, increases in LF (0.0112±0.0101 against 0.0175±0.0208s²Hz⁻¹; p<0.1) and HF (0.0011±0.0008 against 0.0017±0.0020s²Hz⁻¹; p<0.05) were obtained. The possibility of using these indexes to develop an ischaemic-episode classifier was also tested. Results suggest that wavelet analysis provides useful information for the assessment of dynamic changes and patterns of HRV during myocardial ischaemia.     

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.     

基于小波变换的小鼠QRS复合波检测

采用二进小波变换与斜率和幅度相结合的方法，对小鼠QRS复合波进行检测。根据小鼠QRS复合波的特点，采用Daubechics小波为母函烽，按照ECG的频谱特点选用尺度因子，对有噪声污染和形态变异的QRS复合波进行了检测。结果表明：小波变换对小鼠QRS复合波的检测是一种有效的方法。