Classification of Arteriovenous Fistula Stenosis Using Shunt Murmurs Analysis and Support Vector Machine
Classification of Arteriovenous Fistula Stenosis Using Shunt Murmurs Analysis and Support Vector Machine
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利用分流杂音分析和支持向量机对动静脉瘘狭窄进行分类
DOI：
10.1007/978-3-319-93659-8_81
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Furuya Ken’ichi
中科院分区：
文献类型：
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作者：
Higashi Daisuke;Tanaka Keiko;Shin Satoko;Nishijima Keisuke;Furuya Ken’ichi
血液透析患者通常被提供有分流器，但是可能发生诸如血管狭窄的问题。血液透析患者通过听分流杂音检查自身分流功能是有效的。然而，手动判断分流功能是困难的，并且需要经验。因此，分流功能的自动分类来分析分流杂音可能是检查分流功能的有效方法。在这项研究中，我们提出了一种方法来分类分流狭窄使用支持向量机（SVM）。我们使用从超声诊断设备获得的阻力指数（RI）作为类别标签和归一化的互相关系数，频率功率和梅尔频率倒谱系数（MFCC）的比率作为SVM分类器学习的特征。结果，支持向量机对RI的分类准确率低于人类判断的准确率。
Hemodialysis patients are generally provided with a shunt, but problems such as stenosis of the blood vessel can occur. It is effective for hemodialysis patients to check their own shunt function by listening to shunt murmurs. However, manually judging shunt function is difficult and requires experience. Therefore, automatic classification of shunt functions to analyze shunt murmurs could be an effective method for checking shunt function. In this study, we propose a method to classify shunt stenoses using support vector machine (SVM). We use the resistance index (RI) obtained from the ultrasonic diagnostic equipment as a class label and the normalized cross correlation coefficient, the ratio of the frequency power and Mel-Frequency Cepstral Coefficients (MFCC) as the feature learned by the SVM classifier. As a result, the accuracy of classification of RI by SVM was lower than that obtained by human judgment.