A novel approach for separation among normal and heart murmurs sounds based on Phonocardiogram (PCG) analysis is introduced in this paper. The purpose of this work is to find the appropriate algorithm able to detect heart failures. Different features have been extracted from time and frequency domains. After the normalization step, the Principal Component Analysis algorithm is used for data reduction and compression. Support Vectors Machine (SVM), and k-Nearest Neighbors (kNN) classifiers were used with different kernels and number of neighbors in the classification step. Simulation results obtained from different databases are compared. The developed system gave good results when applied to different datasets. The accuracy of 96%, and 100% for the first, and the second dataset respectively were obtained. The algorithm shows its effectiveness in separation between normal and pathological cases.

本文介绍了一种基于心电图（PCG）分析的正常杂音和心脏杂音分离方法。这项工作的目的是找到能够检测出心力衰竭的适当算法。从时域和频域提取了不同的特征。在标准化步骤之后，将主成分分析算法用于数据缩减和压缩。支持向量机（SVM）和k最近邻（kNN）分类器在分类步骤中使用了不同的内核和邻居数。比较了从不同数据库获得的仿真结果。当应用于不同的数据集时，开发的系统给出了良好的结果。第一个和第二个数据集的准确度分别为96％和100％。