Heart valve disorders (HVDs) are the major causes of cardiovascular diseases (CVD), which may be detected at the early stage using routine auscultation examination. The phonocardiogram (PCG) is a graphical representation of the physiological condition of the heart, which differs with respect to heart diseases. It is closely related to valve functionality which provides vital information for the diagnosis of CVD. However, visual inspection of PCG is tedious and error-prone, which makes it necessary and urgent to develop an automated system for the detection of HVDs with PCG recordings. In the present study we propose a novel method for the identification and classification of normal and abnormal non-segmented PCG recordings based on hybrid signal processing tools and deterministic learning theory. First, PCG signal and its first derivative are decomposed into a set of frequency subbands with a number of decomposition levels by using the tunable Q-factor wavelet transform method. Second, fast and adaptive multivariate empirical mode decomposition decomposes the subbands of the PCG signal and its derivative into scale-aligned intrinsic mode components (IMFs). The first two IMFs are extracted, which contain most of the energy of the PCG signal and its derivative and are considered to be the predominant IMFs. Third, Shannon energy is used to extract the characteristic envelope of predominant IMFs. The properties associated with the nonlinear PCG system dynamics are preserved. They are utilized to derive features, which demonstrate significant difference in PCG system dynamics between normal versus abnormal individual heartbeats. Fourth, neural networks are then used to model, identify and classify PCG system dynamics between normal and abnormal PCG signals based on deterministic learning theory. Finally, experiments have been carried out on a publicly available PCG database to verify the effectiveness of the proposed method, which include two types of classification, one for binary classification (normal vs. abnormal) and the other for multi-class classification (normal vs. aortic stenosis vs. mitral regurgitation vs. mitral stenosis vs. mitral valve prolapse). The overall average accuracy for binary, four-class and five-class classification are reported to be 97.75, 98.69 and 98.48%, respectively. The proposed method has obtained the highest overall accuracy in comparison to other state-of-the-art approaches using the same database, which can serve as an assistant diagnostic tool for the automated detection of HVDs in clinical applications.

心脏瓣膜疾病（HVDs）是心血管疾病（CVD）的主要原因，可在早期通过常规听诊检查发现。心音图（PCG）是心脏生理状况的图形表示，在心脏病方面有所不同。它与阀门功能密切相关，后者为CVD诊断提供了重要信息。然而，PCG的目视检查是繁琐且容易出错的，这使得迫切需要开发一种自动系统，以检测具有PCG记录的HVD。在本研究中，我们提出了一种基于混合信号处理工具和确定性学习理论的正常和异常非分段PCG记录的识别和分类的新方法。第一的，通过使用可调Q因子小波变换方法，将PCG信号及其一阶导数分解为具有多个分解级别的一组频率子带。其次，快速和自适应的多元经验模式分解将PCG信号的子带及其导数分解为比例对齐的固有模式分量（IMF）。提取前两个IMF，它们包含PCG信号及其导数的大部分能量，被认为是主要的IMF。第三，香农能量用于提取主要IMF的特征包络。保留与非线性PCG系统动力学相关的属性。利用它们来推导特征，这些特征证明了正常和异常个人心跳之间PCG系统动力学的显着差异。第四，然后，基于确定性学习理论，使用神经网络对正常和异常PCG信号之间的PCG系统动力学进行建模，识别和分类。最后，在公开的PCG数据库上进行了实验，以验证所提出方法的有效性，该方法包括两种类型的分类，一种用于二元分类（正常与异常），另一种用于多类分类（正常与异常）。主动脉瓣狭窄与二尖瓣反流vs二尖瓣狭窄与二尖瓣脱垂）。据报告，二分类，四分类和五分类的整体平均准确度分别为97.75、98.69和98.48％。与使用同一数据库的其他最新方法相比，该方法获得了最高的总体准确性，