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图学学报

• 视觉与图像 • 上一篇    下一篇

二维心音图特征提取与识别方法的研究

摘 要:针对心音的特征提取问题,将一维心音信号转换成二维心音图,采用图#br# 像处理技术提取心音的图像特征。本文首先对一维心音信号进行小波降噪和幅值归一化,将#br# 处理后的心音信号转换成具有统一性和可比性的二维心音图,并进行预处理;然后结合心音#br# 生理意义和二维心音图的图像特征,对能表征二维心音图生理信息的图像特征进行分析研#br# 究,重点研究二维心音图纵横坐标比和拐点序列码特征;最后,基于纵横坐标比、拐点序列#br# 码、小波分解系数3 个特征,探讨利用欧氏距离和支持向量机(SVM)两种识别方法进行#br# 二维心音图分类和身份识别的可行性。实验结果表明,3 种特征都可以实现二维心音图的分#br# 类识别,其中拐点序列码识别率最高;这种基于图像处理的二维心音图分类和身份识别方法#br# 具有明显的可行性和实用性,拥有广阔的应用前景。#br# 关 键 词:二维心音图;图像处理 ;特征提取;识别   

  • 出版日期:2015-04-30 发布日期:2015-03-30

Research on Methods of Feature Extraction and Recognition of Two-Dimensional Phonocardiogram

Abstract: The one-dimensional heart sound signal is converted into a two-dimensional#br# phonocardiogram, then image feature of heart sounds based on image processing technology in a#br# two-dimensional phonocardiogram is extracted. Firstly the wavelet noise reduction and amplitude#br# normalization of one-dimensional heart sound by one-dimensional signal processing method are#br# realized, and then heart sounds after the treatment are converted into two-dimensional#br# phonocardiogram with uniformity and comparability, and pretreatment. And the image#br# characteristics of two-dimensional phonocardiogram are analyzed, which is characterization of#br# heart sounds’ physiological information combining with heart sounds’ physiological significance#br# and two-dimensional phonocardiogram’s image features, and the focus is on vertical and#br# horizontal ratio of coordinate and sequence code of inflection point. At last, the feasibility of#br# classification and identification is explored of 2D-PCG using Euclidean distance and Support#br# Vector Machine (SVM) based on vertical and horizontal ratio of coordinate, sequence code of#br# inflection point and wavelet coefficients. Experimental results show that the three features can#br# achieve the classification and recognition of the two-dimensional phonocardiogram, and inflection#br# point sequence code gets the highest recognition rate. The method of 2D-PCG classification and#br# identification based on a two- image processing has the feasibility and practical applicability, and#br# has broad application prospects.#br# Key words: two-dimensional phonocardiogram; image processing; feature extraction;#br# recognition   

  • Online:2015-04-30 Published:2015-03-30

摘要: 针对心音的特征提取问题,将一维心音信号转换成二维心音图,采用图
像处理技术提取心音的图像特征。本文首先对一维心音信号进行小波降噪和幅值归一化,将
处理后的心音信号转换成具有统一性和可比性的二维心音图,并进行预处理;然后结合心音
生理意义和二维心音图的图像特征,对能表征二维心音图生理信息的图像特征进行分析研
究,重点研究二维心音图纵横坐标比和拐点序列码特征;最后,基于纵横坐标比、拐点序列
码、小波分解系数3 个特征,探讨利用欧氏距离和支持向量机(SVM)两种识别方法进行
二维心音图分类和身份识别的可行性。实验结果表明,3 种特征都可以实现二维心音图的分
类识别,其中拐点序列码识别率最高;这种基于图像处理的二维心音图分类和身份识别方法
具有明显的可行性和实用性,拥有广阔的应用前景。

关键词: 二维心音图, 图像处理 , 特征提取, 识别

Abstract: The one-dimensional heart sound signal is converted into a two-dimensional
phonocardiogram, then image feature of heart sounds based on image processing technology in a
two-dimensional phonocardiogram is extracted. Firstly the wavelet noise reduction and amplitude
normalization of one-dimensional heart sound by one-dimensional signal processing method are
realized, and then heart sounds after the treatment are converted into two-dimensional
phonocardiogram with uniformity and comparability, and pretreatment. And the image
characteristics of two-dimensional phonocardiogram are analyzed, which is characterization of
heart sounds’ physiological information combining with heart sounds’ physiological significance
and two-dimensional phonocardiogram’s image features, and the focus is on vertical and
horizontal ratio of coordinate and sequence code of inflection point. At last, the feasibility of
classification and identification is explored of 2D-PCG using Euclidean distance and Support
Vector Machine (SVM) based on vertical and horizontal ratio of coordinate, sequence code of
inflection point and wavelet coefficients. Experimental results show that the three features can
achieve the classification and recognition of the two-dimensional phonocardiogram, and inflection
point sequence code gets the highest recognition rate. The method of 2D-PCG classification and
identification based on a two- image processing has the feasibility and practical applicability, and
has broad application prospects.

Key words: two-dimensional phonocardiogram, image processing, feature extraction;
recognition