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Compression of ECG Signals Based on DWT and Exploiting the Correlation between ECG Signal Samples

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DOI: 10.4236/ijcns.2014.71007    5,125 Downloads   7,651 Views   Citations

ABSTRACT

This paper presents a hybrid technique for the compression of ECG signals based on DWT and exploiting the correlation between signal samples. It incorporates Discrete Wavelet Transform (DWT), Differential Pulse Code Modulation (DPCM), and run-length coding techniques for the compression of different parts of the signal; where lossless compression is adopted in clinically relevant parts and lossy compression is used in those parts that are not clinically relevant. The proposed compression algorithm begins by segmenting the ECG signal into its main components (P-waves, QRS-complexes, T-waves, U-waves and the isoelectric waves). The resulting waves are grouped into Region of Interest (RoI) and Non Region of Interest (NonRoI) parts. Consequently, lossless and lossy compression schemes are applied to the RoI and NonRoI parts respectively. Ideally we would like to compress the signal losslessly, but in many applications this is not an option. Thus, given a fixed bit budget, it makes sense to spend more bits to represent those parts of the signal that belong to a specific RoI and, thus, reconstruct them with higher fidelity, while allowing other parts to suffer larger distortion. For this purpose, the correlation between the successive samples of the RoI part is utilized by adopting DPCM approach. However the NonRoI part is compressed using DWT, thresholding and coding techniques. The wavelet transformation is used for concentrating the signal energy into a small number of transform coefficients. Compression is then achieved by selecting a subset of the most relevant coefficients which afterwards are efficiently coded. Illustrative examples are given to demonstrate thresholding based on energy packing efficiency strategy, coding of DWT coefficients and data packetizing. The performance of the proposed algorithm is tested in terms of the compression ratio and the PRD distortion metrics for the compression of 10 seconds of data extracted from records 100 and 117 of MIT-BIH database. The obtained results revealed that the proposed technique possesses higher compression ratios and lower PRD compared to the other wavelet transformation techniques. The principal advantages of the proposed approach are: 1) the deployment of different compression schemes to compress different ECG parts to reduce the correlation between consecutive signal samples; and 2) getting high compression ratios with acceptable reconstruction signal quality compared to the recently published results.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

M. Abo-Zahhad, T. Abdel-Hamid and A. Mohamed, "Compression of ECG Signals Based on DWT and Exploiting the Correlation between ECG Signal Samples," International Journal of Communications, Network and System Sciences, Vol. 7 No. 1, 2014, pp. 53-70. doi: 10.4236/ijcns.2014.71007.

References

[1] P. S. Addison, “Wavelet Transforms and the ECG: A Review,” Physiological Measurement, Vol. 26, No. 5, 2005, pp. 155-199.
http://dx.doi.org/10.1088/0967-3334/26/5/R01
[2] S. Olmos, M. Millán, J. García and P. Laguna, “ECG Data Compression with the Karhunen-Loève Transform,” Proceedings of Computers in Cardiology, Indianapolis, IEEE Press, Piscataway, 1996, pp. 253-256.
[3] B. R. S. Reddy and I. S. N. Murthy, “ECG Data Compression Using Fourier Descriptions,” IEEE Transactions on Biomedical Engineering, Vol. 33, No. 4, 1986, pp. 428-434.
http://dx.doi.org/10.1109/TBME.1986.325799
[4] N. Ahmed, P. J. Milne and S. G. Harris, “Electrocardiographic Data Compression Via Orthogonal Transforms,” IEEE Transactions on Biomedical Engineering, Vol. BME-22, No. 6, 1975, pp. 484-487.
http://dx.doi.org/10.1109/TBME.1975.324469
[5] J. H. Husøy and T. Gjerde, “Computationally Efficient Subband Coding of ECG Signals,” Medical Engineering & Physics, Vol. 18, No. 2, 1996, pp. 132-142.
http://dx.doi.org/10.1016/1350-4533(95)00028-3
[6] C. P. Mammen and B. Ramamurthi, “Vector Quantization for Compression of Multichannel ECG,” IEEE Transactions on Biomedical Engineering, Vol. 37, No. 9, 1990, pp. 821-825.
http://dx.doi.org/10.1109/10.58592
[7] J. Chen, S. Itoh and T. Hashimoto, “ECG Data Compression by Using Wavelet Transform,” IEICE Transactions on Information and Systems, Vol. E76-D, No. 12, 1993, pp. 1454-1461.
[8] D. Haugland, J. Heber and J. Husøy, “Optimization Algorithms for ECG Data Compression,” Medical & Biological Engineering & Computing, Vol. 35, No. 4, 1997, pp. 420-424. http://dx.doi.org/10.1007/BF02534101
[9] R. Nygaard, G. Melnikov and A. K. Katsaggelos, “Rate Distortion Optimal ECG Signal Compression,” Proceedings of International Conference on Image Processing, Kobe, 24-28 October 1999, pp. 348-351.
[10] P. de Chazal, S. Palreddy and W. J. Tompkins, “Automatic Classification of Hear-Beats Using ECG Morphology and Heartbeat Interval Features,” IEEE Transactions on Biomedical Engineering, Vol. 51, No. 7, 2004, pp. 1196-1206.
http://dx.doi.org/10.1109/TBME.2004.827359
[11] S. Jalaleddine, C. Hutchens, R. Strattan and W. Coberly, “ECG Data Compression Techniques—A Unified Approach,” IEEE Transactions on Biomedical Engineering, Vol. 37, No. 4, 1990, pp. 329-343.
http://dx.doi.org/10.1109/10.52340
[12] D. L. Donoho, “Denoising by Soft-Thresholding,” IEEE Transactions on Information Theory, Vol. 41, No. 3, 1995, pp. 613-627. http://dx.doi.org/10.1109/18.382009
[13] A. Djohan, T. Q. Nguyen and W. J. Tompkins, “ECG Compression Using Discrete Symmetric Wavelets Transform,” IEEE—EMBC and CMBEC, Vol. 14, No. 2, 1997, pp. 167-168.
[14] M. L. Hilton, “Wavelet and Wavelet Packet Compression of Electrocardiograms,” IEEE Transactions on Biomedical Engineering, Vol. 44, No. 5, 1997, pp. 394-402.
http://dx.doi.org/10.1109/10.568915
[15] A. Al-Shrouf, M. Abo-Zahhad and S. M. Ahmed, “A Novel Compression Algorithm for Electrocardiogram Signals Based on the Linear Prediction of the Wavelet Coefficients,” Digital Signal Processing, Vol. 13, No. 4, 2003, pp. 604-622.
http://dx.doi.org/10.1016/S1051-2004(02)00031-3
[16] D. Tohumoglu and K. Erbil Sezgin, “ECG Signal Compression by Multiiteration EZW Coding for Different Wavelets and Thresholds,” Computers in Biology and Medicine, Vol. 37, No. 2, 2007, pp. 173-182.
http://dx.doi.org/10.1016/S1051-2004(02)00031-3
[17] N. Boukhennoufa, K. Benmahammed, M. A. Abdi and F. Djeffal, “Wavelet-Based ECG Signals Compression Using SPIHT Technique and VKTP Coder,” International Conference on Signals, Circuits and Systems, Medenine, 6-8 November 2009, pp. 1-5.
[18] M. Abo-Zahhad, S. M. Ahmed and A. Zakaria, “ECG Signal Compression Technique Based on Discrete Wavelet Transform and QRS-Complex Estimation,” Signal Processing—An International Journal (SPIJ), Vol. 4, No. 2, 2011, pp. 138-160.
[19] M. S. Hossain and N. Amin, “ECG Compression Using Sub-Band Thresholding of the Wavelet Coefficients,” Australian Journal of Basic and Applied Sciences, Vol. 5, No. 5, 2011, pp. 739-749.
[20] S. A. Chouakri, O. Djaafri1 and A. Taleb-Ahmed, “Wavelet Transform and HUFFMAN Coding Based Electrocardiogram Compression Algorithm: Application to Telecardiology,” 24th IUPAP Conference on Computational Physics, Journal of Physics Conference Series, Vol. 454, No. 1, 2013, pp. 1-16.
[21] R. S. Istepanian, L. J. Hadjileontiadis and S. M. Panas, “ECG Data Compression Using Wavelets and Higher Order Statistics Methods,” IEEE Transactions on Information Technology in Biomedicine, Vol. 5, No. 2, 2001, pp. 108-115. http://dx.doi.org/10.1109/4233.924801

  
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