Frontiers of Electrical and Electronic Engineering >

2009 , Vol. 4 >Issue 1: 10 - 14

DOI: https://doi.org/10.1007/s11460-009-0009-y

RESEARCH ARTICLE

Compression algorithm for electrocardiograms based on sparse decomposition

  • Chunguang WANG , 1 ,
  • Jinjiang LIU 2 ,
  • Jixiang SUN 1
Expand
  • 1. College of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China
  • 2. Department of Computer Science, Nanyang Normal University, Nanyang 473061, China

Published date: 05 Mar 2009

Copyright

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg
Fold

Abstract

Sparse decomposition is a new theory in signal processing, with the advantage in that the base (dictionary) used in this theory is over-complete, and can reflect the nature of a signal. Thus, the sparse decomposition of signal can obtain sparse representation, which is very important in data compression. The algorithm of compression based on sparse decomposition is investigated. By training on and learning electrocardiogram (ECG) data in the MIT-BIH Arrhythmia Database, we constructed an over-complete dictionary of ECGs. Since the atoms in this dictionary are in accord with the character of ECGs, it is possible that an extensive ECG datum is reconstructed by a few nonzero coefficients and atoms. The proposed compression algorithm can adjust compression ratio according to practical request, and the distortion is low (when the compression ratio is 20∶1, the standard error is 5.11%). The experiments prove the feasibility of the proposed compression algorithm.

Key words： sparse decomposition; orthogonal matching pursuit (OMP); K-SVD; electrocardiogram (ECG)

Cite this article

Chunguang WANG , Jinjiang LIU , Jixiang SUN . Compression algorithm for electrocardiograms based on sparse decomposition[J]. Frontiers of Electrical and Electronic Engineering, 2009 , 4(1) : 10 -14 . DOI: 10.1007/s11460-009-0009-y

References

Publishing order | Descend order by publishing year | Descend order by cited within
1
Cheng W, Fang B, Shen Y. A new ECG compression method based on 2-step VQ of DCT coefficients. Chinese Journal of Biomedical Engineering, 2005, 24(6): 690–695 (in Chinese)

2
Kou P, Fang B, Shen Y. A reconstruction quality controlled compression algorithm for ECG signal. Beijing Biomedical Engineering, 2004, 23(2): 109–111 (in Chinese)

3
Wang X Y, Meng J. Hybrid 2-D ECG compression method based on wavelet transform. Acta Biophysica Sinica, 2006, 22(3): 217–224 (in Chinese)

4
Alesanco A, Olmos S, Istepanian R, Garcia J. A novel real-time multilead ECG compression and de-noising method based on the wavelet transform. In: Proceedings of Computers in Cardiology, 2003, 593–596

5
Mallat S G, Zhang Z F. Matching pursuits with time-frequency dictionaries. IEEE Transactions on Signal Processing, 1993, 41(12): 3397–3415

DOI

6
Pati Y C, Rezaiifar R, Krishnaprasad P S. Orthogonal matching pursuits: recursive function approximation with applications to wavelet decomposition. In: Proceedings of the 27th Asilomar Conference in Signals, Systems and Computers. California: IEEE Computer, 1993: 1–5

7
Aharon M, Elad M, Bruckstein A M. K-SVD: an algorithm for designing of overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing, 2006, 54(11): 4311–4322

DOI

8
Chen S S, Donoho D L, Saunders M A. Atomic decomposition by basis pursuit. SIAM Review, 2001, 43(1): 129–159

DOI

9
Kreutz-Delgado K, Murray J F, Rao B D, Engan K, Lee T W, Sejnowski T J. Dictionary learning algorithms for sparse representation. Neural Computation, 2003, 15(2): 349–396

DOI

Options
Outlines

/

〈 〉