Computer-Based Detection and Analysis of Heart Sound and Murmur

To develop a digital algorithm that detects first and second heart sounds, defines the systole and diastole, and characterises the systolic murmur. Heart sounds were recorded in 300 children with a cardiac murmur, using an electronic stethoscope. A Digital algorithm was developed for detection of first and second heart sounds. R-waves and T-waves in the electrocardiography were used as references for detection. The sound signal analysis was carried out using the short-time Fourier transform. The first heart sound detection rate, with reference to the R-wave, was 100% within 0.05–0.2R-R interval. The second heart sound detection rate between the end of the T-wave and the 0.6R-R interval was 97%. The systolic and diastolic phases of the cardiac cycle could be identified. Because of the overlap between heart sounds and murmur a systolic segment between the first and second heart sounds (20–70%) was selected for murmur analysis. The maximum intensity of the systolic murmur, its average frequency, and the mean spectral power were quantified. The frequency at the point with the highest sound intensity in the spectrum and its time from the first heart sound, the highest frequency, and frequency range were also determined. This method will serve as the foundation for computer-based detection of heart sounds and the characterisation of cardiac murmurs.     

造影血管边缘检测方法研究

本文讨论了一种新的血管造影图像边缘检测方法。这一方法是首先调整正规化滤波器的正规化参数用以平滑血管造影图像，去除低于这参数尺度的细节噪声，再利用小波变换提取血管的边缘。我们的结果表明，在相当大的正规化参数范围内，有效地去除不需要的细节噪声的同时，还能很好的保证提取的血管边缘的精度。与单独使用小波变换或递推正规化滤波器相比，该方法的优点是既弥补了小波变换去除相关噪声差的弱点，又改善了正规化滤波器局部分辨率差的缺点。     

脑电中癫痫波的自动检测与分类──一种分层次、多方法整合的途径

脑电中癫痫波的自动检测与分类是临床上很有意义的工作。本文采用一种‘分层次、多方法整合’途径，把自适应预测、小波变换、人工神经网络、模糊识别、专家系统等信号处理技术有机地结合在处理的不同层次（如初筛、分解、特征提取、识别与分类等）上，建立起一种新的分析框架。临床数据的初步测试结果比较满意     

X射线图像中的角点检测设计

角点检测是计算机图像处理中经常会遇到的问题。和通常所处理的图像相比，X射线图像成像质量较低，因此常用的角点检测方法不适于X射线图像的角点检测。该文针对X射线图像的特点，首先设计了一个用于角点检测的棋盘状模板，并针对该模板图像设计了一个通用的检测方法。该检测方法采用分块处理的策略，首先采用自动阈值提取包含角点的每个子区域，然后采用了一个简单实用的边缘算子进行边缘检测得到边缘点，在此基础上采用Hough变换进行直线拟合得到四条边，它们的交点即为所需的角点。该方法较好地解决了由于X射线图像质量比较差而给角点检测带来的困难。实验结果表明，该方法具有很强的鲁棒性。     

小波分析在医学中的应用

小波分析方法是一种新的信号分析方法 ,由于其具有多分辨分析的特点 ,能较好地突出信号局部特征 ,在微弱、背景噪声较强的随机信号分析中具有重要的意义。本文综述了这一新方法在诸如 EEG、EP、ECG、医学图像等生物医学信号中的广泛应用 ,并对各自效果进行了分析。最后 ,对小波变换在生物医学信号处理中的发展作了展望。     

EMG power spectra of cervical muscles in lateral flexion and comparison with sagittal and oblique plane activities

The purpose of this study was to calculate electromyographic (EMG) power spectra of the sternocleidomastoid (SCM), the splenius capitis (SPL) and the trapezius (TRP) muscles from both sides of the body in bilateral lateral flexion and compare them with the EMG spectral characteristics of the same muscles in the sagittal and oblique planes recorded from the same subjects in the same experimental session but reported elsewhere in the literature. Forty normal and healthy young adults (21 males, 19 females) volunteered for the study. The subjects were prepared and positioned appropriately to exert linearly ramping isometric exertion aided by visual feedback against a rigid and load cell instrumental device. Exertions were carried out in a random order. The maximal voluntary contraction was reached within a 5-s test period. The torque and EMG from the SCM, SPL and TRP muscles were sampled bilaterally at a rate of 1 kHz. The EMG data were subjected to fast Fourier transform analysis. During lateral flexion, the torque generated by females was significantly lower than males (P<0.01) and represented at 75% of male torque. Contrary to flexion/extension and oblique plane activities, the ipsilateral SCM was most active with highest power and up to four dominant frequencies demonstrating four distinct peaks. The bandwidth of this muscle progressively increased with the grade of contraction up to 400 Hz, similar to flexion/extension with median frequency (MF) spread between 21 and 109 Hz for males and 78 and 99 Hz for females. The ipsilateral SPL was the second most active muscle, which maintained a narrow bandwidth (200 Hz) and one dominant frequency, rising in power with increasing grade of contraction. Flexion and extension caused symmetrical activity bilaterally. The results of MANOVA revealed a significant main effect of gender, activity direction, grade of contraction and individual muscles (P<0.001). There was a significant interaction between muscle and activity direction (P<0.001) implying significantly different MF and mean power frequency if the direction of exertion was changed. Male and female subjects responded to direction differently (P<0.001). Electronic Publication     

Fourier transform infrared spectroscopy of follicular fluids from large and small antral follicles

Fourier transform infrared spectroscopy (FTIR) was used to obtain 'biochemical fingerprints' for the constitution of follicular fluids from large and small antral luteinized follicles (n = 54 pairs). All samples gave reproducible characteristic biological infrared absorption spectra, with recognizable amide I protein vibrations and acyl vibrations from fatty acids. Discriminant function analysis of the first derivative FTIR spectra, together with hierarchical cluster analysis used to construct a dendrogram, showed fluid from large follicles formed a homogeneous closely related cluster, whilst that from small follicles was distinct from the large, and heterogeneous in nature. The large follicle fluids showed closer biochemical similarity to each other than to the corresponding fluid taken from small matched follicles. An artificial neural network was trained and following validation with an independent test set, successfully distinguished follicular fluids from large and small follicles. The sex steroid concentrations in the fluids from large and small follicles were significantly different. These results show that fluid from large follicles is distinct in biochemical nature from that from small follicles, but the degree of homogeneity implies size-specific changes take place. These may have consequences for the developmental potential of the oocyte.     

小波变换在医学图像融合中的应用

图像融合是医学图像处理中的关键技术。文中探讨了基于小波变换的医学图像融合方法。首先对源图像进行小波多尺度分解,然后采用基于窗口的融合规则进行小波系数融合,最后通过小波逆变换重构融合图像。实验结果表明,该方法能在保留原图像信息的情况下增强融合图像的细节信息。     

基于小波系数熵的表面肌电信号识别

目的:对表面肌电信号进行分类识别。方法:30名健康的志愿者参加数据采集。每名志愿者用右手臂完成两个动作:前臂内旋和前臂外旋。在每个动作中,采集一组表面EMG信号。总共获得30组内旋和30组外旋的表面EMG信号。然后,运用小波包系数熵构成特征向量,用Bayes决策对两种模式信号进行分类识别。结果:当信号长度达350ms后,正确识别率达到100%。结论:采用小波包系数熵可以有效地提取表面EMG信号的特征信息,达到控制前臂假肢的目的。