APPLICATION OF CONTINUOUS WAVELET TRANSFORM IN THE ANALYSIS OF ELECTROCARDIOGRAM SIGNALS

A. Al Maamari, E. Balakrishnan, S. Narasimman
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Abstract

The electrocardiogram is known as a primary and powerful diagnostic tool that provides all essential information about the health of our heart. The feature extraction of electrocardiogram, such as R-peak detection, is the central core of any electrocardiogram analysis. Study of electrocardiogram in wavelet domain using continuous wavelet transform with well-known wavelets and other proposed wavelets for this investigation is found to be helpful and yields reasonably reliable results. In order to validate this method, we apply it to several MIT-BIH database records. The continuous wavelet transform with one of the proposed wavelets namely, Mxr-1, achieves 99.97 % sensitivity, 99.89 % positive predictivity, and 0.135 % detection error for accurate detection of R peaks in comparison with the well-known standard wavelets such as Morlet, Mexican hat and Daubechies 4 and two other proposed mother wavelets Mxr-2 and Mxr-3. AMS Subject Classification: 42C40, 65T60, 92C55
连续小波变换在心电图信号分析中的应用
心电图被认为是一种主要而强大的诊断工具,它提供了关于我们心脏健康的所有基本信息。心电图的特征提取,如r峰检测,是任何心电图分析的核心。用连续小波变换和常用的小波及其他常用小波对心电图进行小波域研究,得到了较为可靠的结果。为了验证该方法,我们将其应用于几个MIT-BIH数据库记录。与Morlet、Mexican hat和Daubechies 4等众所周知的标准小波以及另外两个母小波Mxr-2和Mxr-3相比,其中一个小波Mxr-1的连续小波变换在准确检测R峰方面达到了99.97%的灵敏度、99.89%的正预测性和0.135%的检测误差。AMS学科分类:42C40、65T60、92C55
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