计算机与现代化 ›› 2016, Vol. 0 ›› Issue (6): 36-39.doi: 10.3969/j.issn.1006-2475.2016.06.008

• 应用与开发 • 上一篇    下一篇

基于支持向量机的心音信号自动识别

  

  1. 河南工业职业技术学院电子信息工程系,河南南阳473000
  • 收稿日期:2015-12-02 出版日期:2016-06-16 发布日期:2016-06-17
  • 作者简介:郭春璐(1987-),女,河南登封人,河南工业职业技术学院电子信息工程系助教,硕士,研究方向:计算机应用; 岳小冰(1989-),女,河南许昌人,助教,学士,研究方向:计算机应用。
  • 基金资助:
    河南省科技计划项目(142102210557); 南阳市科技计划项目(KJGG 38, KJGG 51)

Automatic Recognition of Heart Sound Signal Based on Support Vector Machine

  1. Department of Electronics and Information Engineering, Henan Polytechnic Institute, Nanyang 473000, China
  • Received:2015-12-02 Online:2016-06-16 Published:2016-06-17

摘要: 心音信号识别对心血管疾病的诊断具有重要意义,为了提高心音信号的识别性能,提出一种基于支持向量机的心音信号自动识别方法。首先采用小波分析对心音信号进行降噪预处理,然后提取心音信号的Mel频率倒谱系数作为心音信号特征,最后采用支持向量机建立心音信号分类器,对采集心音信号数据的识别性能进行验证。实验结果表明,本文方法的心音信号平均识别率高达93%以上,可以准确识别正常和各种异常的心音信号。

关键词: 小波分析, 心音信号识别, 支持向量机, 特征提取

Abstract: Recognition of heart sound signal plays an important role in the diagnosis of heart disease. In order to improve the performance of heart sound recognition, this paper presents an automatic recognition method of heart sound signal based on support vector machine. Firstly, wavelet analysis is used to reduce the noise of the heart sound signal, and then Mel frequency cepstral coefficients are extracted as the feature of heart sound, finally, the support vector machine (SVM) is used to build classifier of heart sound signal and the performance is tested by using heart sound data. The results show that the recognition accuracy of the heart sound signal is as high as 93% for the proposed method, it can automatically and correctly identify the normal and abnormal heart sound signal.

Key words:  wavelet analysis, heart sound signal recognition, support vector machine, feature extraction

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