【作者】 邱雅竹；

【导师】 莫智文；

【作者基本信息】 四川师范大学 ， 基础数学， 2005， 硕士

【摘要】 本文主要从标准粗糙集出发介绍了将单个非空有限论域上的粗糙集推广到两个非空有限论域上形成的两个论域上的粗糙集, 并对其性质进行了讨论, 在此基础上进一步讨论了两个论域上广义覆盖粗糙集理论,并且研究了心电分类在其上的应用。由于在分类之前,必须得到心电信号的波形特征,而R 波的检测是得到心电信号其他波形特征的基础,其定位准确与否直接影响后续处理的正确性。本文尝试了两种自动识别R 波的方法: 一种方法因考虑到噪声对R波的检测有很大的影响,所以首先对含噪声的心电信号用正交小波包去噪,然后采用二进小波变换对ECG 中QRS 波进行提取,在提取之后,对23尺度上的小波变换进行处理,使得与R 波所对应的模极值有较大幅度的增大,而其余的模极值增大幅度很小,相对于R 波所对应的模极值幅度是在减小,从而使检测更容易。该方法使用MIT-BIH 数据库进行了检验,取得满意结果。另一种方法采用一种新的小波去识别R 波,基于心电信号的特征点对应于Mexican-hat 小波变换的极值点,我们使用Mexican-hat 小波检测心电信号的特征点,为心电信号分析提供了新的检测手段。该方法简单,对心电信号特征点定位准确,快速。经MIT-BIH 心电数据库检验,QRS 波的检测率达到99.9%。为心电图的自动诊断奠定了一定的基础。更多还原

【Abstract】 In this paper, as a generalization of pawalak Rough set model, we introduce the Rough set model over two universes and discuss the properties. And its applications to the ECG signal classified as well. Prior to the ECG signal classified, it is needed to obtain the characteristic of the ECG signal. The detection of R wave laid the foundation for the other characteristic of the ECG signal. So, we try two methods to detect R waves: one is the preprocessing technique of ECG data performing with orthonormal wavelet packets. Wavelet decomposition of signal of the preprocessed signal was obtained by Mallat’s pyramidal algorithm. Then we deal with the detail signal of 23 scales, the extremes corresponding with R wave have been considerable increased, the QRS complex detection rate has been improved. The result of ORS detection rate examined by the MIT-BIT arrhythmia database is satisfactory. Another method is that we use Mexican-hat wavelet transform to detect characteristic points of ECG signal based on the characteristic points corresponding with the extremes of Mexican-hat wavelet transform; it offers a new detection means to ECG signal analysis. This method is simple and it was proved to be accurate and reliable. The correct rate of ORS detection rata examined by the MIT-BIT arrhythmia database rises up to 99.9%..更多还原