Echocardiography frames quantification by empirical mode decomposition method

Hesam Aliniazare, H. Behnam, E. Fatemizadeh, Z. Sani
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Abstract

In this study a new method is proposed for quantification of cardiac muscle motions in echocardiography frames based on empirical mode decomposition (EMD) and manifold learning method. EMD algorithm is able to extract intrinsic mode functions (IMF) from a signal. In the first bi-dimension intrinsic mode functions (BIMF) of echocardiography frames myocardial is shown with more details than the second BIMF and the second BIMF shows more details than the third BIMF. By using manifold learning method, quantification difference between each pair of consecutive frames in the first, second and third BIMF series (similarities between the frames were extracted). Acquired trajectories of three manifolds for ischemie hearts are similar to each other but they are different for healthy hearts. This finding can be used for classifying ischemie and healthy hearts.
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超声心动图帧量化的经验模态分解方法
本文提出了一种基于经验模态分解(EMD)和流形学习方法的超声心动图图像心肌运动量化方法。EMD算法能够从信号中提取出固有模态函数(IMF)。在超声心动图框架的第一个二维内禀模态函数(BIMF)中,心肌比第二个BIMF显示更多的细节,第二个BIMF比第三个BIMF显示更多的细节。采用流形学习方法,量化第一、第二、第三个BIMF序列中每对连续帧之间的差异(提取帧之间的相似度)。缺血心脏的三流形获得轨迹相似,而正常心脏的三流形获得轨迹不同。这一发现可用于对缺血和健康心脏进行分类。
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