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Noninvasive detection of coronary artery disease based on heart sounds

机译:基于心脏声音的冠状动脉疾病的非侵入性检测

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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,通过在胸腔上的特殊位置处于特殊位置的高度敏感声音传感器阵列来检测正常人和冠状动脉疾病(CAD)的四个通道心脏声音。使用小波变换和神经网络分析所获取的信号。小波变换是一种高度适用的方法,可以将复杂的心脏声音分离成各种频率段,并且还可以在心动周期中提供事件的时间定位。计算每个通道的四个平均电源比在基于WT系数的四个频率段处的整个循环心声对舒张周期心声的平均功率比。四个通道的十六个电源比分解为径向基函数(RBF)神经网络的十六个参数输入模式。培训后,神经网络可以自动诊断CAD。

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