S1和S2共振峰频率在心音分类识别中的应用

发布时间：2018-05-07 22:09

  本文选题：心音 + 共振峰频率　； 参考：《南京邮电大学学报(自然科学版)》2017年05期

【摘要】：针对心音身份识别过程中心音特征提取的难点,提出了一种以第一心音(S1)和第二心音(S2)共振峰频率作为特征的心音分类识别方法。对原始心音通过小波变换进行去噪处理;基于归一化平均香农能量的分段算法对心音信号分段获得S1和S2的时域波形;采用线性预测编码(LPC)的方法分别提取S1和S2的共振峰频率;结合S1和S2共振峰频率构成心音的特征向量,并采用支持向量机(SVM)的分类方法对心音的特征向量进行分类识别。实验结果显示,S1和S2共振峰频率能够很好地表征心音信号的稳定性和唯一性,以S1和S2共振峰频率作为心音特征进行分类识别具有非常高的识别精度,这为基于心音特征的身份识别技术以及心脏疾病诊断方法提供了可靠的理论基础。
[Abstract]:Aiming at the difficulty of heart sound feature extraction in the process of heart sound identification, this paper presents a method of heart sound classification and recognition with the resonance peak frequency of the first heart tone S1) and the second heart tone S2) as the features. The original heart sound is de-noised by wavelet transform, and the time domain waveforms of S1 and S2 are obtained based on the segmented algorithm of normalized average Shannon energy. The resonance peak frequencies of S1 and S2 are extracted by linear predictive coding (LPC) method, and the eigenvector of heart sound is constructed by combining the frequency of S _ 1 and S _ 2 resonance peaks, and the feature vectors of heart sound are classified and recognized by support vector machine (SVM) classification method. The experimental results show that the frequency of S _ 1 and S _ 2 resonance peaks can well characterize the stability and uniqueness of the heart sound signal, and the recognition accuracy is very high by using the S _ 1 and S _ 2 resonance peak frequencies as the heart sound characteristics. This provides a reliable theoretical basis for the identification of heart sounds and the diagnosis of heart disease.
【作者单位】： 南京邮电大学电子与光学工程学院;
【基金】：国家自然科学基金(61271334,61073115) 江苏省研究生培养创新工程(SJCX17_0229)资助项目
【分类号】：R540.4;TN911.7
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本文编号：1858661