Heart valve disease (HVD) is one of the common cardiovascular diseases. Heart sound is an important physiological signal for diagnosing HVDs. This paper proposed a model based on combination of basic component features and envelope autocorrelation features to detect early HVDs. Initially, heart sound signals lasting 5 minutes were denoised by empirical mode decomposition (EMD) algorithm and segmented. Then the basic component features and envelope autocorrelation features of heart sound segments were extracted to construct heart sound feature set. Then the max-relevance and min-redundancy (MRMR) algorithm was utilized to select the optimal mixed feature subset. Finally, decision tree, support vector machine (SVM) and k-nearest neighbor (KNN) classifiers were trained to detect the early HVDs from the normal heart sounds and obtained the best accuracy of 99.9% in clinical database. Normal valve, abnormal semilunar valve and abnormal atrioventricular valve heart sounds were classified and the best accuracy was 99.8%. Moreover, normal valve, single-valve abnormal and multi-valve abnormal heart sounds were classified and the best accuracy was 98.2%. In public database, this method also obtained the good overall accuracy. The result demonstrated this proposed method had important value for the clinical diagnosis of early HVDs.
心脏瓣膜病（HVD）是常见的心血管疾病之一，心音是用于检测心脏瓣膜病的重要生理信号。本文提出了一种基于心音基本成分特征和包络自相关特征的联合分类模型，以检测早期心脏瓣膜病。本文首先使用经验模态分解（EMD）对5 min心音信号去噪，分割成心音信号样本，并提取心音信号样本的基本成分特征和包络自相关特征，联合上述两类特征构建心音特征集；然后使用最大相关最小冗余（MRMR）算法选择最优混合特征；最后分别使用决策树、支持向量机（SVM）和K最邻近（KNN）分类器对正常心音和早期心脏瓣膜病心音进行分类。经临床数据验证，本文模型分类正常心音和异常心音的准确率达到99.9%，分类正常心音、半月瓣异常心音和房室瓣异常心音的准确率达到99.8%，分类正常心音、单瓣膜异常心音和多瓣膜异常心音的准确率达到98.2%。在公开数据集上，本文模型也取得了较好的分类结果。综上所述，本文方法对早期心脏瓣膜病的诊断具有重要的参考价值。.
Keywords: Envelope autocorrelation; Feature selection; Heart sound features; Heart valve disease.