Empirical Mode Decomposition - An Introduction

被引:0
|
作者
Zeiler, A. [1 ]
Faltermeier, R. [2 ]
Keck, I. R. [1 ]
Tome, A. M. [3 ]
Puntonet, C. G. [4 ]
Lang, E. W. [1 ]
机构
[1] Univ Regensburg, Dept Biophys, CIML Grp, D-93040 Regensburg, Germany
[2] Univ Hosp Regensburg, Clin Neurosurgery, D-93040 Regensburg, Germany
[3] Univ Aveiro, IEETA, DETI, P-3810 Aveiro, Portugal
[4] Univ Granada, ETSIIT, DATC, E-18071 Granada, Spain
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中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Due to external stimuli, biomedical signals are in general non-linear and non-stationary. Empirical Mode Decomposition in conjunction with a Hilbert spectral transform, together called Hilbert-Huang Transform, is ideally suited to extract essential components which are characteristic of the underlying biological or physiological processes. The method is fully adaptive and generates the basis to represent the data solely from these data and based on them. The basis functions, called Intrinsic Mode Functions (IMFs) represent a complete set of locally orthogonal basis functions whose amplitude and frequency may vary over time. The contribution reviews the technique of EMD and related algorithms and discusses illustrative applications.
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