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Detection and Diagnosis of Dilated and Hypertrophic Cardiomyopathy by Echocardiogram Sequences Analysis

机译:超声心动图序列分析检测和诊断扩张和肥厚性心肌病

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Automating the detection and diagnosis of cardiovascular diseases using echocardiogram sequences is a challenging task because of the presence of speckle noise, less information and movement of chambers. In this paper an attempt has been made to classify the normal hearts, and hearts affected by dilated cardiomyopathy (DCM) and hypertrophic cardiomyopathy (HCM) by automating the segmentation of left ventricle (LV). The segmented LV from the diastolic frames of echocardiogram sequences alone is used for extracting features. The statistical features and Zernike moment features are obtained from extracted diastolic LV and classified using the classifiers namely support vector machine (SVM), back propagation neural network (BPNN) and probabilistic neural network (PNN). An intensive examination over 60 echocardiogram sequences reveals that the proposed method performs well in classifying normal hearts and hearts affected by DCM and HCM. Among the classifiers used the BPNN classifier with the combination of Zernike moment features gave an highest accuracy of 92.08 %.
机译:使用超声心动图序列自动化心血管疾病的检测和诊断是一个具有挑战性的任务,因为存在斑点噪声,较少的腔室的信息和运动。在本文中,通过自动化左心室(LV)的分割来分类正常心脏和肥厚性心肌病(HCM)的正常心脏和肥厚性心肌病(HCM)的心脏。单独使用超声心动图序列的舒张框架的分段的LV用于提取特征。统计特征和Zernike矩特征是从提取的舒张式LV获得,并使用分类器进行分类,即支持向量机(SVM),后传播神经网络(BPNN)和概率神经网络(PNN)。超过60超声心动图序列的密集检查表明,该方法在分类正常心脏和受DCM和HCM影响的心中表现良好。在分类器中,使用具有Zernike Moment特征的组合的BPNN分类器,其最高精度为92.08%。

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