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Noninvasive detection of coronary artery disease based on heart sounds

机译:基于心音的无创检测冠状动脉疾病

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In this study, four channel heart sounds of normal persons and patients with coronary artery disease (CAD) were detected by a highly sensitive sound sensor array placed in special positions on the thorax. The acquired signals were analyzed using wavelet transform and neural networks. The wavelet transform is a highly applicable method to separate complicated heart sounds into various frequency segments and can also give time localization of the event in the cardiac cycle. Each and every channel's four average power ratios of whole cycle heart sound to diastolic period heart sound at four frequency segments based on WT coefficients were calculated. The sixteen power ratios of four channels decomposed into sixteen parameters input pattern of radial basis function (RBF) neural networks. After training, the neural networks can diagnose the CAD automatically.
机译:在这项研究中,通过放置在胸部特殊位置的高度灵敏的声音传感器阵列检测到正常人和冠心病(CAD)患者的四通道心音。使用小波变换和神经网络分析获取的信号。小波变换是一种非常有用的方法,可以将复杂的心音分成不同的频率段,并且还可以在心动周期中给出事件的时间定位。基于WT系数,计算了四个频率段上每个周期的全周期心音与舒张期心音的四个平均功率比。将四个通道的十六个功率比分解为径向基函数(RBF)神经网络的十六个参数输入模式。训练后,神经网络可以自动诊断CAD。

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