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http://dx.doi.org/10.5391/JKIIS.2011.21.5.653

ECG signal compression based on B-spline approximation  

Ryu, Chun-Ha (경북대학교 IT대학 전자공학부)
Kim, Tae-Hun (경북대학교 IT대학 전자공학부)
Lee, Byung-Gook (동서대학교 컴퓨터정보공학부)
Choi, Byung-Jae (대구대학교 전자공학부)
Park, Kil-Houm (경북대학교 IT대학 전자공학부)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.21, no.5, 2011 , pp. 653-659 More about this Journal
Abstract
In general, electrocardiogram(ECG) signals are sampled with a frequency over 200Hz and stored for a long time. It is required to compress data efficiently for storing and transmitting them. In this paper, a method for compression of ECG data is proposed, using by Non Uniform B-spline approximation, which has been widely used to approximation theory of applied mathematics and geometric modeling. ECG signals are compressed and reconstructed using B-spline basis function which curve has local controllability and control a shape and curve in part. The proposed method selected additional knot with each step for minimizing reconstruction error and reduced time complexity. It is established that the proposed method using B-spline approximation has good compression ratio and reconstruct besides preserving all feature point of ECG signals, through the experimental results from MIT-BIH Arrhythmia database.
Keywords
B-spline approximation; Knot vector; ECG Signal; ECG data compression;
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1 S. Jalaleddine, C. Hutchens, R. Strattan. and W. Coberly, "ECG data compression techniques-A Unified approach," IEEE Trans. Biomedical Engineering, vol. 37, pp. 329-343, 1990.   DOI   ScienceOn
2 M. Unser, A. Aldroubi, and M. Eden, "B-spline signal processing: Part I-Theory," IEEE Trans. Signal Processing, vol. 41, no. 2, pp. 821-833, 1993.   DOI   ScienceOn
3 M. Karczewicz, M. Hamalainen, M. Gabbouj, Multiple knot spline approximation for ECG data compression, Proc. IEEE Workshop on Nonlinear Signal and Image Processing, Neos-Marmaras, Halkidiki, Greece, 20-22 June 1995, pp487-490.
4 Yaniv Zigel, Atnon Cohen, Amos Katz, "The Weighted Diagnostic Distortion Measure for the ECG Signal Compression", IEEE Trans. Biomed. Eng., vol 47, no. 11, Nov. 2000.
5 F. Enseleit and F. Duru, "Long-term continuous external electrocardiographic recording: A review," Europace, vol. 8, no. 4, pp. 255-266, 2006.   DOI   ScienceOn
6 J. Abenstein and W. Tompkins, "A new data-reduction algorithm for real-time ECG analysis," IEEE Trans. Biomedical Engineering, vol. BME-29, no. 1, pp. 43-48, 1982.   DOI
7 B. J. Schijvennaars, G. Van Herpen, and J. A. Kors, "Intraindividual variability in electrocardiograms," Journal of Electrocardiology, vol. 41, no. 3, pp. 190-196, 2008.   DOI   ScienceOn
8 H. J. Kim, R. F. Yazicioglu, P. Merken, C. Van Hoof, and H. J. Yoo, "ECG signal compression and classification algorithm with quad level vector for ECG holter system," IEEE Trans. Information Technology in Biomedicine, vol. 14, no. 1, pp. 93-100, 2010.   DOI
9 F. Jager, I. Koren, and L. Gyergyek, "Multiresolut ional representation and analysis of ECG waveforms," Proceedings of Computers in Cardiology, pp. 547-550, 1990.
10 M. Benmalek and A. Charef, "Digital fractional order operators for R-wave detection in electrocardiogram signal," IET Signal Processing, vol. 3, no. 5, pp. 381-391, 2009.   DOI   ScienceOn
11 Q. Zhang, A. I. Manriquez, C. Medigue, Y. Papelier, and M. Sorine, "An algorithm for robust and efficient location of T-wave ends inelectrocardiograms," IEEE Trans. Biomedical Engineering, vol. 53, no. 12, pp. 2544-2552, 2006.   DOI
12 J. P. Martinez, R. Almeida, S. Olmos, A. P. Rocha, and P. Laguna, "A wavelet-based ECG delineator: Evaluation on standard databases," IEEE Trans. Biomedical Engineering, vol. 51, no. 4, pp. 570-581, 2004.   DOI   ScienceOn