Study on reconstruction of rectal sensation based on wavelet packet analysis and SVM strategy

To control anal incontinence, we have developed an artificial anal sphincter system with sensor feedback. The artificial anal sphincter system is a novel hydraulic-electric muscle which mainly comprises an artificial anal sphincter, a wireless power supply subsystem, and a rectal sensation reconstruction subsystem. To investigate the features of the patients’ rectal sensation, we have developed an in vitro experimental platform of artificial anal sphincter. In vitro experiments have been performed, and demonstrate that the traditional threshold method is not suitable for predicting the time for defecation. The traditional threshold method only uses single-dimensional pressure time series which may contain a few interdependent components simultaneously. A wavelet packet analysis algorithm is employed to extract the feature vector of the rectal pressure signal, then the rectal sensation prediction model is constructed based on a support vector machine for defecation pattern recognition. The results show that the proposed method is an effective approach for the reconstruction of patients’ rectal sensation.     

基于小波包变换的癫痫脑电棘波检测

为了能够较好地实现癫痫患者脑电的棘波检测,提出一种将棘波物理特征（幅度、频率）和小波包变换结合的算法,用于癫痫患者脑电信号的棘波检测。首先利用小波包变换对癫痫脑电信号进行小波包分解,将脑电波频率（0~30 Hz）划分为3层;其次根据脑电波的频率范围重构第三层节点频率S（3, 0）（0~10.85 Hz）、S（3, 1）（10.85~21.7 Hz）、S（3, 2）（21.7~32.55 Hz）的脑电信号;最后取棘波的幅度作为检测阈值分别提取癫痫患者健康期、癫痫发作间期及癫痫发作期的棘波。实验结果证明,当数据的采样频率为173.61 Hz、信号长度为23.6 s时,该算法能够提取不同癫痫患者在不同时期的棘波信号,该算法棘波的误检率为12.02%、漏检率为11.70%。因此,本文所采用的算法在癫痫棘波检测中具有良好的效果。     

A method of tumor classification based on wavelet packet transforms and neighborhood rough set

Tumor classification is an important application domain of gene expression data. Because of its characteristics of high dimensionality and small sample size (SSS), and a great number of redundant genes not related to tumor phenotypes, various feature extraction or gene selection methods have been applied to gene expression data analysis. Wavelet packet transforms (WPT) and neighborhood rough sets (NRS) are effective tools to extract and select features. In this paper, a novel approach of tumor classification is proposed based on WPT and NRS. First the classification features are extracted by WPT and the decision tables are formed, then the attributes of the decision tables are reduced by NRS. Thirdly, a feature subset with few attributes and high classification ability is obtained. The experimental results on three gene expression datasets demonstrate that the proposed method is effective and feasible.     

Classification of surface EMG signals using optimal wavelet packet method based on Davies-Bouldin criterion

In this paper we present an optimal wavelet packet (OWP) method based on Davies-Bouldin criterion for the classification of surface electromyographic signals. To reduce the feature dimensionality of the outputs of the OWP decomposition, the principle components analysis was employed. Then we chose a neural network classifier to discriminate four types of prosthesis movements. The proposed method achieved a mean classification accuracy of 93.75%, which outperformed the method using the energy of wavelet packet coefficients (with mean classification accuracy 86.25%) and the fuzzy wavelet packet method (87.5%).     

小波分析理论在图像重建中的应用

本文对小波分析理论在图像重建中的应用进行了具体分析 ,主要是用于二维图像重建的代数重建法 (ART)和滤波反投影 (FBP)法 ,并对小波分析理论与直接体积重建的结合问题作了一些展望     

基于小波分析的医学图像融合技术及其效果评价

多模医学图像融合技术是近年兴起的一种新技术，在医学影像技术学和放射医学领域中有极为重要的应用价值。本文中我们使用小波分析算法有效的实现了基于像素级的多重图像融合，并给出了量化的评判指标。实验结果表明，基于小波分析的医学图像融合技术能有效的保留原始多源图像的边缘和纹理特征。避免融合图像出现平均化。     

基于小波分析的医学图像压缩技术进展

作为图像存诸和传输系统(picture archiving & communication system,PACS)的关键技术之一,医学图像压缩算法的优劣对PACS的性能有着重要的影响,小波分析由于其多分辨率分析特性而在医学图像压缩中得到了广泛应用.从小波变换医学图像压缩、小波包变换医学图像压缩和多小波变换医学图像压缩三个方面综述了小波医学图像压缩方法及其进展,总结对比了这些方法的优点和缺陷,并针对其不足之处提出了改进方向.     

基于小波分析的红外乳腺图像去噪与增强的实验研究

将小波的多分辨率分析技术运用到红外乳腺图像的降噪增强处理以改善图像质量.运用不同的阈值量化策略实现图像降噪.再引入增益因子,采用基于小波变换的增强算法突出肿块阴影.通过实验将直接增强和去噪后再增强的图像进行对比可以得到清晰度更高的图像.经处理后的乳腺图像,为临床提供了更细致明确的信息,有助于提高诊断水平.     

利用ECG信号检测睡眠呼吸暂停的小波包分析方法

文章根据睡眠呼吸暂停与心率变化的关系。阐述了从心电图中检测睡眠呼吸暂停的方法。该方法首先通过对心率信号进行小波包变换，提取特征向量，然后根据特征向量的变化检测出睡眠呼吸暂停的位置和分布。结果表明，该方法物理意义明确，诊断结果精度高，为睡眠呼吸暂停综合症的早期诊断、监护及预后评估提供了新的分析工具。     

基于小波变换的中医推拿扌衮法作用力信号能量分析

目的 分析中医推拿法作用力各频率段的能量分布,揭示扌衮法的动力学特点。方法 测量6位推拿专家及6位初学者的扌衮法推拿作用力信号,利用小波变换进行频带划分,计算各频率段的能量并进行统计分析。对垂直作用力和水平作用力构造18个特征量,提出了总体评价系数R。结果 专家的扌衮法推拿作用力信号在0~0.406 25 Hz的低频段集中了近70%的能量,另有约20%的能量集中在1.625~3.25 Hz频率段。6位推拿专家总体评价系数R均大于0.70,6位初学者的总体评价系数R均小于0.70,两者具有较显著的差异。结论 扌衮法作用力各频率段的能量分布体现了法“柔和”性及周期性特点。当测试者扌衮法推拿技术动作规范且总体评价系数R大于0.70时,可认为已较好地掌握了扌衮法推拿技术。     

一种基于小波域主成分分析的心电压缩算法

目的为降低心电信号存储和传输的数据量,并克服传统心电压缩方法只利用导联内相关性的劣势,本文提出一种基于小波域主成分分析和分层编码(w PCA_LC)的压缩方法。方法首先通过心电电极获取12通道心电数据,对所有通道的心电信号做小波变换,每个尺度下的小波系数组成小波系数矩阵,在每个系数矩阵上做主成分分析(principal component analysis,PCA),之后对小波系数小的主成分做[位置增量,数据]的编码方式,其他主成分采用霍夫曼编码,最后使用本文算法压缩圣彼得堡心率失常数据库。结果实验表明,在均方根误差为5.2%时,本文算法的压缩比为71,远高于基于稀疏分解的方法和基于小波变换阈值选择的方法。结论基于小波域主成分分析的心电压缩算法对多导联心电信号具有较好的压缩性能。     

EMD和SVM结合的脑电信号分类方法

脑电(EEG)癫痫波的自动检测与分类在临床医学上具有重要意义。针对EEG信号的非平稳特点,本文提出了一种基于经验模式分解(EMD)和支持向量机(SVM)的EEG分类方法。首先利用EMD将EEG信号分成多个经验模式分量,然后提取有效特征,最后用SVM对EEG信号进行分类。结果表明,该方法对癫痫发作间歇期和发作期EEG的分类效果比较理想,识别率达到99%。     

基于支持向量机的中医舌图像质量评价研究

目的在中医舌诊客观化研究实践中,现有舌象仪所采集的大量舌图像不符合临床诊察要求,这将直接影响到舌象分析的最终结果。本文主要是对舌图像质量评价进行研究,为临床上图像的准确挑选提供帮助。方法首先分析中医诊断学中望舌的标准,提取基于自然场景统计的特征、纹理、颜色和几何特征,然后通过支持向量机(support vector machines,SVM)进行分类,并通过分类准确率验证此方法能否为临床上图像的准确挑选提供帮助。结果提取的舌象特征可以较好地完成对舌图像质量的评价,并且能准确挑选出临床上能使用的舌图像。结论中医舌图像的质量评价具有可行性,这一方法将有望应用在新一代的舌象采集装置中,为中医舌图像辅助分析提供高质量的参考数据。     

Multi-level basis selection of wavelet packet decomposition tree for heart sound classification

Wavelet packet transform decomposes a signal into a set of orthonormal bases (nodes) and provides opportunities to select an appropriate set of these bases for feature extraction. In this paper, multi-level basis selection (MLBS) is proposed to preserve the most informative bases of a wavelet packet decomposition tree through removing less informative bases by applying three exclusion criteria: frequency range, noise frequency, and energy threshold. MLBS achieved an accuracy of 97.56% for classifying normal heart sound, aortic stenosis, mitral regurgitation, and aortic regurgitation. MLBS is a promising basis selection to be suggested for signals with a small range of frequencies.     

基于欧几里德几何距离矩阵分析的三维颅面复原评价方法

目的:探索欧几里德几何距离矩阵分析(EDMA)在三维颅面复原效果评价的应用.方法:采用兰玉文等开发的三维颅面鉴定仪对120例(女男性各60例)辽宁汉族成人重建后的三维颅骨进行相貌复原,复原后的三维面貌与真实的三维面貌利用WinEDMA软件进行EDMA分析.结果:EDMA分析显示,女性复原组,非参数bootstrap检验T=1.791,P=0.001,形状差异矩阵(FDM)比值在0.95～1.05的占71％,男性复原组,非参数bootstrap检验T=1.741,P=0.001,FDM比值在0.95～1.05的占70％,线段FDM比值小于0.95和大于0.95的部分主要集中在五官区,进一步分析显示眼部的特征点对复原效果影响最大.结论:利用该方法复原出的面貌与真实面貌存在差异,但总体看复原面貌与真实面貌大体相似,EDMA能够对复原的效果进行评价并找出复原结果中误差较大的位置.     

小波先验在OSL重建算法中的应用

背景：MAP(最大后验)统计重建方法可以在重建过程中引入合适的先验知识达到去除噪声的目的。 目的：根据小波系数的统计特性及能量平衡的原理对高频信息做相应的处理,并将多尺度的小波先验应用到OSL重建算法中以去除噪声。 方法：实验从“变换域”的思想出发,在小波域上根据小波系数的统计特性及能量平衡原理对不同尺度的高频信息做相应的处理,并利用处理后的小波系数进行小波重建。 结果与结论：基于小波先验的OSL算法比ML-EM算法重建的图像与测试模型的误差变小、相关性变大、噪声变少,重建图像变得比较平滑,视觉效果较清楚。     

Evaluation of blood access dysfunction based on a wavelet transform analysis of shunt murmurs

We investigated shunt murmurs based on wavelet transform analysis as a new method for assessing vascular access function. In the present study, in patients with venous stenosis near an arteriovenous fistula (A-V fistula), a sensor was placed at different positions around the stenosis and shunt murmur signals obtained using a measurement system were subjected to time–frequency analysis based on wavelet transforms. The shunt murmurs obtained from the stenotic region closely represented some features of murmurs that are often referred to as “high-pitch” murmurs in the clinical setting. In contrast, shunt murmurs obtained about 5 cm downstream of the stenotic region closely represented some features of murmurs that are often referred to as “low-pitch” murmurs in the clinical setting. Furthermore, with the aim of extending the lifespan of arteriovenous grafts (A-V grafts) by detecting and treating stenotic lesions before the A-V graft becomes occluded, we evaluated the possibility of utilizing the present shunt murmur analysis for monitoring stenosis in such A-V grafts. When shunt murmurs from patients with A-V grafts were analyzed, the results suggested that the blood flow through the venous anastomosis of the graft was the most turbulent. This present method whereby blood flow in an A-V fistula is assessed based on the frequency distribution on a time–frequency plane by wavelet transform analysis is advantageous because findings are not markedly affected by sensor attachment. Furthermore, because the sensor is attached using an adhesive collar, measurements can be taken over a short period of time before each dialysis session.     

基于三维重建技术及有限元分析的脊柱骨密度测量及其意义

目的探讨基于三维重建技术及有限元分析的测量骨密度的方法。方法以1具脊柱标本(C3～L5)进行高速CT薄层扫描,在Mimics中进行每个椎体的三维重建,在Ansys中进行体网格划分,在Mimics中赋予10、400种材料属性,输出至Ansys计算骨骼中每一种材料属性的体单元的体积,根据CT扫描的Hu值与骨密度的经验公式,计算标本质量及密度,对照组以电子天平测量(C3～L5)的质量。进行统计学处理。结果(1)对照组、10种及400种材料属性组C3～L5的密度值分别为0.62±0.09、0.5813±0.0806、0.5813±0.0805g/cm3;(2)单因素方差分析:对照组与赋予10、400种材料属性各试验组比较的P值均大于0.1,试验组组别之间P=0.997。结论(1)本试验方法可定量测量骨骼密度及其密质骨及松质骨密度值;(2)赋予骨骼10种材料属性即可达到测量要求;(3)本试验为骨密度与有限元分析的统一作初步准备。