A Novel Compression Algorithm for Electrocardiogram Signals based on Wavelet Transform and SPIHT

Authors: Sana Ktata, Kaïs Ouni, Noureddine Ellouze

Abstract:

Electrocardiogram (ECG) data compression algorithm is needed that will reduce the amount of data to be transmitted, stored and analyzed, but without losing the clinical information content. A wavelet ECG data codec based on the Set Partitioning In Hierarchical Trees (SPIHT) compression algorithm is proposed in this paper. The SPIHT algorithm has achieved notable success in still image coding. We modified the algorithm for the one-dimensional (1-D) case and applied it to compression of ECG data. By this compression method, small percent root mean square difference (PRD) and high compression ratio with low implementation complexity are achieved. Experiments on selected records from the MIT-BIH arrhythmia database revealed that the proposed codec is significantly more efficient in compression and in computation than previously proposed ECG compression schemes. Compression ratios of up to 48:1 for ECG signals lead to acceptable results for visual inspection.

Keywords: Discrete Wavelet Transform, ECG compression, SPIHT.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1058063

References:

[1] K. Nguyen-Phi, H. Weinrichterm "ECG signal coding using wavelet transform and binary arithmetic coder," In International Conference on Information, Communications and Signal Processing, ICICS, pp.1344- 1348, Sep. 1997 Singapore.
[2] L.V. Batista, E.U.K. Melcher, L.C. Carvalho, "Compression of ECG signals by optimized quantization of discrete cosine transform coefficients," Elsevier Med. Eng. Phys. 23, pp.127-134, 2001.
[3] H. Lee, K.M. Buckley, "ECG data compression using cut and align beats approach and 2-D transform," IEEE Trans. Biomed. Eng. Vol. 46 No. 5, pp. 556-564, 1999.
[4] M.L. Hilton, "Wavelet and wavelet packets compression of electrocardiogram," IEEE Trans. Biomed. Eng. Vol. 44, No. 5, pp. 394-402, 1997.
[5] S.G. Miaou, S.N. Chao, "Wavelet based lossy-to-lossless ECG compression in a unified vector quantization framework,"IEEE Trans. Biomed. Eng. Vol.52, No. 3, pp. 539-543, 2005.
[6] S.G. Miaou, H.L. Yen, "Multichannel ECG compression using multichannel adaptive vector quantization," IEEE Trans. Biomed. Eng. Vol. 48, No. 10, pp.1203-1207, 2001.
[7] S.G. Miaou, H.L. Yen, C.L. Lin, "Wavelet-based ECG compression using dynamic vector quantization with tree codevectors in single codebook," IEEE Trans. Biomed. Eng. Vol. 49 No. 7, pp. 671-680, 2002.
[8] M. Benzid, F. Marir, A. Boussaad, M. Benyoucef, D. Arar, "Fixed percentage of wavelet coefficients to be zeroed for ECG compression," IEE Electron. Lett. Vol. 39, No. 11, pp.830-831, 2003.
[9] S.M.S. Jalaleddine., "ECG Data Compression Techniques - A Unified Approach", IEEE Trans. on Biomed. Eng., Vol. 37, No. 4.,1990, pp. 329-343.
[10] Al-Shrouf, M. Abo-Zahhad, S. M. Ahmed, "A novel compression algorithm for electrocardiogram signal based on the linear prediction of the wavelet coefficients,"Digital Signal Processing, vol. 13, pp. 604- 622, 2003.
[11] M.L. Hilton, "Wavelet and Wavelet Packet Compression of Electrocardiograms," IEEE Trans. on Biomed. Eng., Vol. 44, No. 5, pp.394-402, May 1997.
[12] Z. Lu, D.Y. Kim, and W.A. Pearlman, "Wavelet Compression of ECG Signals by the Set Partitioning in Hierarchical Trees (SPIHT) Algorithm," IEEE Transactions on Biomedical Engineering, Vol. 47, pp. 849-856, July, 2000.
[13] A. Bilgin, M.W. Marcellin, M.I. Altbach, "Compression of Electrocardiogram Signals using JPEG2000," IEEE Transactions on Consumer Electronics, Vol. 49, No. 4, pp. 833-840, November 2003.
[14] A. Said and W.A. Pearlman, "A New, Fast and Efficient Image Codec Based on Set Partitioning in Hierarchical Trees," IEEE Trans. on Circuits and Systems for Video Technology, Vol. 6, pp. 243-250, June 1996.
[15] A. Said, and W.A. Pearlman, "Image compression using the spatialorientation tree", IEEE Int. Symp. on Circuits and Systems, Chicago, IL, 279-282, 1993.Video Technology, 6(3), 243-250, 1996.