Empirical mode decomposition based support vector machines for microemboli classification

K. Ferroudji, N. Benoudjit, Ayache Bouakaz
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引用次数: 6

Abstract

The classification of circulating microemboli, in the bloodstream, as gaseous or particulate matter is vital for selecting appropriate treatment for patients. Until now, Doppler techniques have shown some limitations to determine clearly the nature of circulating microemboli. The traditional techniques are largely based on the Fourier analysis. In this paper we present new emboli detection method based on Empirical mode decomposition and support vector machine using Radio Frequency (RF) signal instead of Doppler signals.
基于经验模态分解的支持向量机微栓子分类
将血液中循环的微栓子分类为气体或颗粒物质对于为患者选择适当的治疗方法至关重要。到目前为止,多普勒技术在明确确定循环微栓子的性质方面显示出一些局限性。传统的技术主要是基于傅里叶分析。本文提出了一种基于经验模态分解和支持向量机的新型栓子检测方法,采用射频信号代替多普勒信号。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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