经验模态分解在生物医学信号处理中的计算时间研究——以心电图为例

A. Karagiannis, P. Constantinou
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引用次数: 1

摘要

本文从计算时间的角度研究了经验模态分解(EMD)的性能。嵌入式系统中的智能资源分配和管理是通过信号处理技术建模来实现的。经验模态分解的计算时间主要取决于迭代次数和初始未知的内禀模态函数集(IMF)的大小。在先验估计方法的计算时间的线性模型中,引入了一个度量来将这些因素包含到单个变量中。在经验模态分解计算时间研究的相同框架下,评估了噪声分量的影响和预处理技术的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Computation time study in biomedical signal processing with Empirical Mode Decomposition: The case of electrocardiogram
In this paper, a study of the Empirical Mode Decomposition (EMD) performance is presented in terms of computation time. Smart resource allocation and management in embedded systems are facilitated by signal processing techniques modeling for time scheduling of tasks. Empirical Mode Decomposition computation time is mainly determined by the number of iterations and the size of Intrinsic Mode Functions (IMF) set which are unknown at the beginning of the process. A metric is introduced to include these factors into a single variable of a linear model developed to a priori estimate method's computation time. In the same framework of Empirical Mode Decomposition computation time study the effects of noisy components and the application of preprocessing techniques are evaluated.
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