ECG DENOISING AND COMPRESSION BY SPARSE 2D SEPARABLE TRANSFORM WITH OVERCOMPLETE MIXED DICTIONARIES

被引：0
|
作者：
Ghaffari, A. [1 ]
Palangi, H. [1 ]
Babaie-Zadeh, M. [1 ]
Jutten, C. [2 ,3 ]
机构：
[1] Sharif Univ Technol, Dept Elect Engn, Tehran, Iran
[2] GIPSA LAB, Grenoble, France
[3] Inst Univ France, Paris, France
来源：
2009 IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING | 2009年
关键词：
Sparse Representation; Sparse Decomposition; ECG Denosing; Sparse Coding; ECG Compression; WAVELET;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, an algorithm for ECG denoising and compression based on a sparse separable 2-dimensional transform for both complete and overcomplete dictionaries is studied. For overcomplete dictionary we have used the combination of two complete dictionaries. The experimental results obtained by the algorithm for both complete and overcomplete transforms are compared to soft thresholding (for denoising) and wavelet db9/7 (for compression). It is experimentally shown that the algorithm outperforms soft thresholding for about 4dB or more and also outperforms Extended Kalman Smoother filtering for about 2dB in higher input SNRs. The idea of the algorithm is also studied for ECG compression, however it does not result in better compression ratios than wavelet compression.
引用
收藏
页码：440 / +
页数：2
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