Cardiac sound murmurs classification with autoregressive spectral analysis and multi-support vector machine technique
Cardiac sound murmurs classification with autoregressive spectral analysis and multi-support vector machine technique
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DOI：
10.1016/j.compbiomed.2009.10.003
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发表时间：
2010-01-01
影响因子：
7.7
通讯作者：
Jiang, Zhongwei
中科院分区：
文献类型：
--
作者：
Choi, Samjin;Jiang, Zhongwei
本文提出了一种基于归一化自回归功率谱密度（NAR-PSD）曲线和支持向量机（SVM）技术的心音频谱分析方法，用于心音杂音的分类。对6名健康志愿者和34名患者的489个心音信号进行了测试，其中正常心音196个，异常心音293个。选择无其他心脏并发症病史的受试者，用自制的无线电子听诊器系统记录正常的声音信号。将异常声信号分为心房颤动、主动脉瓣关闭不全、主动脉瓣狭窄、二尖瓣返流、二尖瓣狭窄和分裂音等6种心脏瓣膜疾病。这些异常的受试者也不包括其他并存的心脏瓣膜疾病。针对心音信号功率谱密度在频域的形态特征，提出了两个重要的诊断特征Fmax和Fwidth，分别描述了NAR-PSD曲线的最大峰值和阈值（THV）上NAR-PSD曲线交叉点之间的频率宽度。此外，一个二维表示（Fmax，Fwidth）。最后通过实例验证了本文提出的心音频谱包络曲线方法的有效性。然后，支持向量机技术作为分类工具，以识别心音提取的诊断特征。为了检测心音异常和区分心脏杂音，考虑并设计了由六个SVM模块组成的多SVM分类器。使用一个数据集来验证每个多SVM模块的分类性能。结果，用于检测异常和分类六种心脏疾病的六个SVM模块的准确度对于每个SVM模块，对于THV =10-90%显示71-98.9%，对于THV =10-50%显示81.2-99.6%。与建议的心音频谱分析方法，高的分类性能，实现了99.9%的特异性和99.5%的灵敏度在分类正常和异常的声音（心脏疾病）。因此，所提出的方法表现出相对非常高的分类效率，如果SVM模块的设计与考虑THV值。将自回归谱分析和多SVM分类器相结合的心音杂音分类方法应用于心脏瓣膜疾病的分类。(C)2009爱思唯尔有限公司保留所有权利。
In this paper, a novel cardiac sound spectral analysis method using the normalized autoregressive power spectral density (NAR-PSD) curve with the support vector machine (SVM) technique is proposed for classifying the cardiac sound murmurs. The 489 cardiac sound signals with 196 normal and 293 abnormal sound cases acquired from six healthy volunteers and 34 patients were tested. Normal sound signals were recorded by our self-produced wireless electric stethoscope system where the subjects are selected who have no the history of other heart complications. Abnormal sound signals were grouped into six heart valvular disorders such as the atrial fibrillation, aortic insufficiency, aortic stenosis, mitral regurgitation, mitral stenosis and split sounds. These abnormal subjects were also not included other coexistent heart valvular disorder. Considering the morphological characteristics of the power spectral density of the heart sounds in frequency domain, we propose two important diagnostic features Fmax and Fwidth, which describe the maximum peak of NAR-PSD curve and the frequency width between the crossed points of NAR-PSD curve on a selected threshold value (THV), respectively. Furthermore, a two-dimensional representation on (Fmax, Fwidth) is introduced. The proposed cardiac sound spectral envelope curve method is validated by some case studies. Then, the SVM technique is employed as a classification tool to identify the cardiac sounds by the extracted diagnostic features. To detect abnormality of heart sound and to discriminate the heart murmurs, the multi-SVM classifiers composed of six SVM modules are considered and designed. A data set was used to validate the classification performances of each multi-SVM module. As a result, the accuracies of six SVM modules used for detection of abnormality and classification of six heart disorders showed 71-98.9% for THVs=10-90% and 81.2-99.6% for THVs=10-50% with respect to each of SVM modules. With the proposed cardiac sound spectral analysis method, the high classification performances were achieved by 99.9% specificity and 99.5% sensitivity in classifying normal and abnormal sounds (heart disorders). Consequently, the proposed method showed relatively very high classification efficiency if the SVM module is designed with considering THV values. And the proposed cardiac sound murmurs classification method with autoregressive spectral analysis and multi-SVM classifiers is validated for the classification of heart valvular disorders. (C) 2009 Elsevier Ltd. All rights reserved.