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An electrocardiogram signal compression techniques: a comprehensive review

  • Supriya O. RajankarEmail author
  • Sanjay N. Talbar
Article
  • 75 Downloads

Abstract

In spite of development in digital storage and communication technology, the demand for data compression is ever increasing. The ECG data requires about 40–50 MB per channel space for 24-h recording. Limitations of storage size, higher bandwidth and the extra transmission time to these signals over different communication channels force to study an efficient compression algorithm. The primary objective is to retain the most useful clinical information while compressing the ECG signals to an acceptable size. The literature proposes many algorithms to implement ECG compression. It is the observation that, among all, the wavelet-based algorithms provide better compression performance. This paper is a review of most promising algorithms of ECG compression with emphasis to wavelet-based ECG signal compression.

Keywords

Compression ratio ECG Entropy coding EZW Percentage root mean difference SPIHT Quantization Thresholding Wavelet transform 

References

  1. 1.
    Manikandan, M. S., & Dandapat, S. (2014). Wavelet-based electrocardiogram signal compression methods and their performances: A prospective review. Biomedical Signal Process Control, 14(1), 73–107.Google Scholar
  2. 2.
    Hamilton, P. S., & Tompkins, W. J. (1991). Compression of the ambulatory ECG by average beat subtraction and residual differencing. IEEE Transactions on Biomedical Engineering, 38(3), 253–259.Google Scholar
  3. 3.
    Subramanian, B. (2017). ECG signal classification and parameter estimation using multiwavelet. Biomedical Research, 28(7), 3187–3193.Google Scholar
  4. 4.
    David, S. (1998). Data compression—The complete reference. New York: Springer.Google Scholar
  5. 5.
    Wang, X., & Meng, J. (2008). A 2-D ECG compression algorithm based on wavelet transform and vector quantization. Digital Signal Processing, 18(2), 179–188.MathSciNetGoogle Scholar
  6. 6.
    Jalaleddine, S. M. S., Hutchens, C. G., Strattan, R. D., & Coberly, W. A. (1990). ECG data compression techniques-a unified approach. IEEE Transactions on Biomedical Engineering, 37(4), 329–343.Google Scholar
  7. 7.
    Sayood, K. (2000). Introduction to data compression. Burlington: Morgan Kaufmann.zbMATHGoogle Scholar
  8. 8.
    Craven, D., Member, S., Mcginley, B., Kilmartin, L., Jones, E., & Member, S. (2016). Adaptive dictionary reconstruction for compressed sensing of ECG signals. IEEE Journal of Biomedical and Health Informatics, 21(3), 645–654.  https://doi.org/10.1109/JBHI.2016.2531182 Google Scholar
  9. 9.
    Agulhari, C. M., Bonatti, I. S., & Peres, P. L. D. (2013). An adaptive run length encoding method for the compression of electrocardiograms. Medical Engineering & Physics, 35(2), 145–153.Google Scholar
  10. 10.
    Chen, J., Ma, J., Zhang, Y., & Shi, X. (2006). ECG compression based on wavelet transform and Golomb coding. Electronics Letters, 42(6), 322.Google Scholar
  11. 11.
    Martínez-Alajarín, J., Martínez-Rosso, J., & Ruiz-Merino, R. (2008). Encoding technique for binary sequences using vector tree partitioning applied to compression of phonocardiographic signals. Electronics Letters, 44(2), 84.Google Scholar
  12. 12.
    Rajankar, S. O., & Talbar, S. N. (2017). Adaptive vector K-tree partitioning an entropy coder : Application to ECG compression. International Journal of Telemedicine and Clinical Practices Inderscience, 2(3), 215–224.Google Scholar
  13. 13.
    Cox, J. R., Nolle, F. M., Fozzard, H. A., & Oliver, G. C. (1968). AZTEC, a preprocessing program for real-time ECG rhythm analysis. IEEE Transactions on Biomedicine Engineering, 15(2), 128–129.Google Scholar
  14. 14.
    Tompkins, W. J. (2000). Biomedical digital signal processing. Berlin: Springer.Google Scholar
  15. 15.
    Kumar, V., Saxena, S. C., Giri, V. K., & Singh, D. (2005). Improved modified AZTEC technique for ECG data compression: Effect of length of parabolic filter on reconstructed signal. Computers & Electrical Engineering, 31, 334–344.Google Scholar
  16. 16.
    Ishijima, M., Shin, S. B., Hostetter, G. H., & Sklansky, J. (1983). Scan-along polygonal approximation for data compression of Electrocardiograms. IEEE Transactions on Biomedical Engineering, 30(11), 723–729.Google Scholar
  17. 17.
    Alam, S., & Gupta, R. (2014). A DPCM based Electrocardiogram coder with thresholding for real time telemonitoring applications, In. International Conference on Communication and Signal Processing, 2014, 176–180.Google Scholar
  18. 18.
    Bahar, H. B., & Khiabani, Y. S. (2006). Optimal design of DPCM scheme for ECG signal handling. In 6th WSEAS international conference on signal, speech and image processing, Lisbon, Portugal, pp. 156–161.Google Scholar
  19. 19.
    Manikandan, M. S., & Dandapat, S. (2007). Wavelet energy based diagnostic distortion measure for ECG. Biomedical Signal Processing and Control, 2, 80–96.Google Scholar
  20. 20.
    Einarsson, G. (1991). An improved implementation of predictive coding compression. IEEE Transactions on Communications, 39(2), 169–171.Google Scholar
  21. 21.
    Nave, G., & Cohen, A. (1993). ECG compression using long-term prediction. IEEE Transactions on Biomedical Engineering, 40(9), 877–885.Google Scholar
  22. 22.
    Cohen, A., & Zigel, Y. (1998). Compression of multichannel ECG through multichannel long-term prediction. IEEE Engineering in Medicine and Biology Magazine, 17(1), 109–115.Google Scholar
  23. 23.
    Zigel, Y., Cohen, A., & Katz, A. (2000). ECG signal compression using analysis by synthesis coding. IEEE Transactions on Biomedical Engineering, 47(10), 1308–1316.Google Scholar
  24. 24.
    Chen, W. S., Hsieh, L., & Yuan, S. Y. (2004). High performance data compression method with pattern matching for biomedical ECG and arterial pulse waveforms. Computer Methods and Programs in Biomedicine, 74(1), 11–27.Google Scholar
  25. 25.
    Paggetti, C., Lusini, M., Varanini, M., Taddei, A., & Marchesi, C. (1994). A multichannel template based data compression algorithm. Computers in Cardiology, 1994, 629–632.Google Scholar
  26. 26.
    Mammen, C. P., & Ramamurthi, B. (1990). Vector quantization for compression of multichannel ECG. IEEE Transactions on Biomedical Engineering, 37(9), 821–825.Google Scholar
  27. 27.
    Cohen, A., Poluta, M., & Scott-Millar, R. (1990). Compression of ECG signals using vector quantization. In IEEE South African symposium on communications and signal processing, pp. 49–54.Google Scholar
  28. 28.
    Cârdenas-Barrera, J. L., & Lorenzo-Ginori, J. V. (1999). Mean-shape vector quantizer for ECG signal compression. IEEE Transactions on Biomedical Engineering, 46(1), 62–70.Google Scholar
  29. 29.
    Sun, C. C., & Tai, S. C. (2005). Beat-based ECG compression using gain-shape vector quantization. IEEE Transactions on Biomedical Engineering, 52(11), 1882–1888.Google Scholar
  30. 30.
    Miaou, S. G., & Yen, H. L. (2001). Multichannel ECG compression using multichannel adaptive vector quantization. IEEE Transactions on Biomedical Engineering, 48(10), 1203–1206.Google Scholar
  31. 31.
    Miaou, S. G., Yen, H. L., & Lin, C. L. (2002). Wavelet-based ECG compression using dynamic vector quantization with tree codevectors in single codebook. IEEE Transactions on Biomedical Engineering, 49(7), 671–680.Google Scholar
  32. 32.
    Filho, E. B. L., Nouriddine, M., & Bashroush, R. (2009). On ECG signal compression with 1-D multiscale recurrent patterns allied to preprocessing techniques. IEEE Transactions on Biomedical Engineering, 56(3), 896–900.Google Scholar
  33. 33.
    Al-Fahoum, A. S. (2006). Quality assessment of ECG compression techniques using a wavelet-based diagnostic measure. IEEE Transactions on Information Technology in Biomedicine, 10(1), 182–191.Google Scholar
  34. 34.
    Zigel, Y., Cohen, A., & Katz, A. (2000). The weighted diagnostic distortion (WDD) measure for ECG signal compression. IEEE Transactions on Biomedical Engineering, 47(11), 1422–1430.Google Scholar
  35. 35.
    Manikandan, M. S., & Dandapat, S. (2008). Multiscale entropy-based weighted distortion measure for ECG coding. IEEE Signal Processing Letters, 15, 829–832.Google Scholar
  36. 36.
    Kumar, R., Kumar, A., & Pandey, R. K. (2013). Beta wavelet based ECG signal compression using lossless encoding with modified thresholding. Computers & Electrical Engineering, 39(1), 130–140.Google Scholar
  37. 37.
    Ahmed, N., Milne, P. J., & Harris, S. G. (1975). Electrocardiographic data compression via orthogonal transforms. IEEE Transactions on Biomedical Engineering, 22(6), 484–487.Google Scholar
  38. 38.
    Kumar, R., Kumar, A., Singh, G. K., & Lee, H. (2017). Efficient compression technique based on temporal modelling of ECG signal using principle component analysis. IET Science, Measurement and Technology, 11(3), 346–353.Google Scholar
  39. 39.
    Bensegueni, S., & Bennia, A. (2016). ECG signal compression using a sinusoidal transformation of principal components. International Journal of Software Engineering and Its Applications, 10(1), 59–68.Google Scholar
  40. 40.
    Kumar, R., Kumar, A., & Singh, G. K. (2016). Hybrid method based on singular value decomposition and embedded zero tree wavelet technique for ECG signal compression. Computer Methods and Programs in Biomedicine, 129, 135–148.Google Scholar
  41. 41.
    Olmos, S., García, J., Jané, R., & Laguna, P. (1999). ECG signal compression plus noise filtering with truncated orthogonal expansions. Signal Processing, 79(1), 97–115.zbMATHGoogle Scholar
  42. 42.
    Al-Nashash, H. A. M. (1995). A dynamic Fourier series for the compression of ECG using FFT and adaptive coefficient estimation. Medical Engineering & Physics, 17(3), 197–203.Google Scholar
  43. 43.
    Batista, L. V., Uwe, E., Melcher, K., & Carvalho, L. C. (2001). Compression of ECG signals by optimized quantization of discrete cosine transform coefficients. Medical Engineering & Physics, 23(2), 127–134.Google Scholar
  44. 44.
    Borsali, R., Naït-Ali, A., & Lemoine, J. (2004). ECG compression using an ensemble polynomial modeling: Comparison with the DCT based technique. Cardiovascular Engineering, 4(3), 237–244.Google Scholar
  45. 45.
    Benzid, R., Messaoudi, A., & Boussaad, A. (2008). Constrained ECG compression algorithm using the block-based discrete cosine transform. Digital Signal Processing A Review Journal, 18, 56–64.Google Scholar
  46. 46.
    Lee, S., Kim, J., & Lee, M. (2011). A real-time ECG data compression and transmission algorithm for an e-health device. IEEE Transactions on Biomedical Engineering, 58, 2448–2455.Google Scholar
  47. 47.
    Bendifallah, A., Benzid, R., & Boulemden, M. (2011). Improved ECG compression method using discrete cosine transform. Electronics Letters, 47(2), 87.Google Scholar
  48. 48.
    Nunes, J. C., & Nait Ali, A. (2005). ECG compression by modelling the instantaneous module/phase of its DCT. Journal of Clinical Monitoring and Computing, 19(3), 207–214.Google Scholar
  49. 49.
    Khorrami, H., & Moavenian, M. (2010). A comparative study of DWT, CWT and DCT transformations in ECG arrhythmias classification. Expert Systems with Applications, 37(8), 5751–5757.Google Scholar
  50. 50.
    Pandey, A., Singh, B., Singh, B., & Neetu, S. (2016). A joint application of optimal threshold based discrete cosine transform and ASCII encoding for ECG data compression with its inherent encryption. Australasian Physical & Engineering Science in Medicine, 39, 833–855.Google Scholar
  51. 51.
    Sandryhaila, A., Saba, S., Puschel, M., & Kovacevic, J. (2012). Efficient compression of QRS complexes using Hermite expansion. IEEE Transactions on Signal Processing, 60(2), 947–955.MathSciNetzbMATHGoogle Scholar
  52. 52.
    Kovács, P., & Dozsa, T. (2016). ECG signal compression using adaptive Hermite functions. Advances in Intelligent Systems and Computing, 399, 245–254.Google Scholar
  53. 53.
    Blanchett, T., Kember, G. C., & Fenton, G. A. (1998). KLT-based quality controlled compression of single-lead ECG. IEEE Transactions on Biomedical Engineering, 45(7), 942–945.Google Scholar
  54. 54.
    Tai, S. C. (1992). Six-band sub-band coder on ECG waveforms. Medical and Biological Engineering and Computing, 30(2), 187–192.Google Scholar
  55. 55.
    Ramakrishnan, A. G., & Saha, S. (1996). ECG compression by multirate processing of beats. Computers and Biomedical Research, 29(5), 407–417.Google Scholar
  56. 56.
    Blanco-Velasco, M., Cruz-Roldán, F., López-Ferreras, F., Bravo-Santos, Á., & Martínez-Muñoz, D. (2004). A low computational complexity algorithm for ECG signal compression. Medical Engineering & Physics, 26(7), 553–568.Google Scholar
  57. 57.
    Blanco Velasco, M., Cruz Roldán, F., Godino Llorente, J. I., & Barner, K. E. (2004). ECG compression with retrieved quality guaranteed. Electronics Letters, 40(23), 1466.Google Scholar
  58. 58.
    Talbar, S., & Rajankar, S. (2010). An optimized transform for ECG signal compression. ACEEE International Journal on Signal & Image Processing, 01(03), 1–4.Google Scholar
  59. 59.
    Kumar, R., Saini, I., Kumar, R., & Saini, I. (2014). Empirical wavelet transform based ECG signal compression empirical wavelet transform based ECG signal compression. IETE Journal of Research, 2063, 423–431.zbMATHGoogle Scholar
  60. 60.
    Voicu, I., & Borda, M. (2005). New method of filters design for dual tree complex wavelet transform. In ISSCS 2005: international symposium on signals, circuits and systemsproceedings, (Vol. 2, pp. 437–440).Google Scholar
  61. 61.
    Ahmed, S. M., Al-shrouf, A., & Abo-zahhad, M. (2000). ECG data compression using optimal non-orthogonal wavelet transform. Medical Engineering & Physics, 22(1), 39–46.Google Scholar
  62. 62.
    Istepanian, R. S. H., Member, S., & Petrosian, A. A. (2000). Optimal zonal wavelet-based ECG data compression for a mobile telecardiology system. IEEE Transactions on Information Technology in Biomedicine, 4(3), 200–211.Google Scholar
  63. 63.
    Istepanian, R. S. H., Hadjileontiadis, L. J., & Panas, S. M. (2001). ECG data compression using wavelets and higher order statistics methods. IEEE Transactions on Information Technology in Biomedicine, 5(2), 108–115.Google Scholar
  64. 64.
    Rajoub, B. A. (2002). An efficient coding algorithm for the compression of ECG signals using the wavelet transform. IEEE Transactions on Biomedical Engineering, 49(4), 355–362.Google Scholar
  65. 65.
    Al-Shrouf, A., Abo-Zahhad, M., & Ahmed, S. M. (2003). A novel compression algorithm for electrocardiogram signals based on the linear prediction of the wavelet coefficients. Digital Signal Processing, 13, 604–622.Google Scholar
  66. 66.
    Ku, C. T., Hung, K. C., Wang, H. S., & Hung, Y. S. (2007). High efficient ECG compression based on reversible round-off non-recursive 1-D discrete periodized wavelet transform. Medical Engineering & Physics, 29, 1149–1166.Google Scholar
  67. 67.
    Benzid, R., Marir, F., & Bouguechal, N. E. (2007). Electrocardiogram compression method based on the adaptive wavelet coefficients quantization combined to a modified two-role encoder. IEEE Signal Processing Letters, 14(6), 373–376.Google Scholar
  68. 68.
    Manikandan, M. S., & Dandapat, S. (2006). Wavelet threshold based ECG compression using USZZQ and Huffman coding of DSM. Biomedical Signal Processing and Control, 1(2006), 261–270.Google Scholar
  69. 69.
    Manikandan, M. S., & Dandapat, S. (2008). Wavelet threshold based TDL and TDR algorithms for real-time ECG signal compression. Biomedical Signal Processing and Control, 3, 44–66.Google Scholar
  70. 70.
    Abo-Zahhad, M., Al-Ajlouni, A. F., Ahmed, S. M., & Schilling, R. J. (2013). A new algorithm for the compression of ECG signals based on mother wavelet parameterization and best-threshold levels selection. Digital Signal Processing A Review Journal, 23(3), 1002–1011.MathSciNetGoogle Scholar
  71. 71.
    Miaou, S. G., & Lin, C. L. (2002). A quality-on-demand algorithm for wavelet-based compression of electrocardiogram signals. IEEE Transactions on Biomedical Engineering, 49(3), 233–239.Google Scholar
  72. 72.
    Shaou Gang Miaou and Heng Lin Yen. (2000). Quality driven gold washing adaptive vector quantization and its application to ECG data compression. IEEE Transactions on Biomedical Engineering, 47(2), 209–218.Google Scholar
  73. 73.
    Miaou, S. G., & Chao, S. N. (2005). Wavelet-based lossy-to-lossless ECG compression in a unified vector quantization framework. IEEE Transactions on Biomedical Engineering, 52(3), 539–543.Google Scholar
  74. 74.
    Chen, J., & Itoh, S. (1998). A wavelet transform-based ECG compression method guaranteeing desired signal quality. IEEE Transactions on Biomedical Engineering, 45(12), 1414–1419.Google Scholar
  75. 75.
    Tan, C., Zhang, L., & Wu, H. (2018). A novel Blaschke unwinding adaptive Fourier decomposition based signal compression algorithm with application on ECG signals. arXiv:1803.06441v1.
  76. 76.
    Qian, X. L. T., & Zhang, L. (2011). Algorithm of adaptive Fourier decomposition. IEEE Transactions on Signal Processing, 59(12), 5899–5909.MathSciNetzbMATHGoogle Scholar
  77. 77.
    Gao, Y., Ku, M., Qian, T., & Wang, J. (2017). FFT formulations of adaptive fourier decomposition. Journal of Computational and Applied Mathematics, 324, 204–215.MathSciNetzbMATHGoogle Scholar
  78. 78.
    Cetin, A. E., Koymen, H., & Aydin, M. C. (1993). Multichannel ECG data compression by multirate signal processing and transform domain coding techniques. IEEE Transactions on Biomedical Engineering, 40(5), 495–499.Google Scholar
  79. 79.
    Mallat, S. (2013). A wavelet tour of signal processing, In Climate change 2013The physical science basis, Intergovernmental Panel on Climate Change, Ed. Cambridge: Cambridge University Press, pp. 1–30.Google Scholar
  80. 80.
    Mallat, S. G. (1989). A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7), 674–693.zbMATHGoogle Scholar
  81. 81.
    Rioul, O., & Vetterli, M. (1991). Wavelets and signal processing. IEEE Signal Processing Magazine, 8(4), 14–38.Google Scholar
  82. 82.
    Antonini, M., Barlaud, M., Mathieu, P., & Daubechies, I. (1992). Image coding using wavelet transform. IEEE Transactions on Image Processing, 1(2), 205–220.Google Scholar
  83. 83.
    Usevitch, B. E. (2001). A tutorial on modern lossy wavelet image compression: Foundations of JPEG 2000. IEEE Signal Processing Magazine, 18(5), 22–35.Google Scholar
  84. 84.
    Tai, S. C., Sun, C. C., & Yan, W. C. (2005). A 2-D ECG compression method based on wavelet transform and modified SPIHT. IEEE Transactions on Biomedical Engineering, 52(6), 999–1008.Google Scholar
  85. 85.
    Chou, H. H., Chen, Y. J., Shiau, Y. C., & Kuo, T. S. (2006). An effective and efficient compression algorithm for ECG signals with irregular periods. IEEE Transactions on Biomedical Engineering, 53(6), 1198–1205.Google Scholar
  86. 86.
    Kumar, V., & Saxena, S. (2007). Refinement criterion for SPIHT based ECG signal compression. IETE Tech. Rev., 24(3), 147–151.Google Scholar
  87. 87.
    Blanco Velasco, M., Cruz Roldán, F., Godino Llorente, J. I., & Barner, K. E. (2007). Wavelet packets feasibility study for the design of an ECG compressor. IEEE Transactions on Biomedical Engineering, 54(4), 766–769.Google Scholar
  88. 88.
    Benzid, R., Marir, F., Boussaad, A., Benyoucef, M., & Arar, D. (2003). Fixed percentage of wavelet coefficients to be zeroed for ECG compression. Electronics Letters, 39(11), 830.Google Scholar
  89. 89.
    Abo-Zahhad, M., & Rajoub, B. A. (2002). An effective coding technique for the compression of one-dimensional signals using wavelet transforms. Medical Engineering & Physics, 24(4), 185–199.Google Scholar
  90. 90.
    Ahmed, S. M., Al-Ajlouni, A. F., Abo-Zahhad, M., & Harb, B. (2009). ECG signal compression using combined modified discrete cosine and discrete wavelet transforms. Journal of Medical Engineering & Technology, 33(1), 1–8.Google Scholar
  91. 91.
    Biswas, D., Mazomenos, E. B., & Maharatna, K. (2012). ECG compression for remote healthcare systems using selective thresholding based on energy compaction. In Conference proceedings of the international symposium on signals, systems and electronics, pp. 1–6.Google Scholar
  92. 92.
    Ranjeet, K., & Farida (2011). Retained signal energy based optimal wavelet selection for denoising of ECG signal using modified thresholding. In 2011 International conference on multimedia, signal processing and communication technologies, (Vol. 1, pp. 196–199).Google Scholar
  93. 93.
    Chompusri, Y., & Yimman, S. (2009). Energy packing efficiency based threshold level selection for DTW ECG compression. International Journal of Applied Biomedical Engineering, 2(2), 19–28.Google Scholar
  94. 94.
    Amin, N., & Arabia, S. (2011). ECG compression using subband thresholding of the wavelet coefficients. Australian Journal of Basic and Applied Sciences, 5(5), 739–749.Google Scholar
  95. 95.
    Alesanco, Á., García, J., Serrano, P., Ramos, L., & Istepanian, R. S. H. (2006). On the guarantee of reconstruction quality in ECG wavelet codecs. In Annual international conference of the IEEE engineering in medicine and biologyproceedings, (Vol. 1, pp. 6461–6464).Google Scholar
  96. 96.
    El B’charri, O., Latif, R., Elmansouri, K., Abenaou, A., & Jenkal, W. (2017). ECG signal performance de- noising assessment based on threshold tuning of dual- tree wavelet transform. Biomedical Engineering Online, 16, 1–18.Google Scholar
  97. 97.
    Yen, H. L., & Miaou, S. G. (2001). ECG compression using dynamic tree vector quantization in wavelet domain. In: 2001 Conference proceedings of the 23rd annual international conference of the IEEE engineering in medicine and biology society, (Vol. 2, pp. 1892–1895).Google Scholar
  98. 98.
    Manikandan, M. S., & Dandapat, S. (2006). Wavelet based ECG compression with large zero zone quantizer. In 2006 Annual India conference, INDICON.Google Scholar
  99. 99.
    Hilton, M. L. (1997). Wavelet and wavelet packet compression of electrocardiograms. IEEE Transactions on Biomedical Engineering, 44(5), 394–402.Google Scholar
  100. 100.
    Lu, Z., Kim, D. Y., & Pearlman, W. A. (2000). Wavelet compression of ECG signals by the set partitioning in hierarchical trees algorithm. IEEE Transactions on Biomedical Engineering, 47(7), 849–856.Google Scholar
  101. 101.
    Shapiro, J. M. (1993). Embedded image coding using zerotrees of wavelet coefficients. IEEE Transactions on Signal Processing, 41(12), 3445–3462.zbMATHGoogle Scholar
  102. 102.
    Brechet, L., Lucas, M. F., Doncarli, C., & Farina, D. (2007). Compression of biomedical signals with mother wavelet packet selection. IEEE Transactions on Biomedical Engineering, 54(12), 2186–2192.Google Scholar
  103. 103.
    Said, A., & Pearlman, W. A. (1996). A new, fast, and efficient image codec based on set partitioning in hierarchical trees. IEEE Transactions on Circuits and Systems for Video Technology, 6(3), 243–250.Google Scholar
  104. 104.
    Wang, Z., Zhu, P., & Chen, Y. (2008). A 2-D ECG compression algorithm based on modified SPIHT. In Proceedings of 5th international workshop on wearable and implantable body sensor networks, BSN2008, in conjunction with the 5th international summer school and symposium on medical devices and biosensors, ISSS-MDBS 2008, pp. 305–309.Google Scholar
  105. 105.
    Kumar, R., Kumar, A., & Singh, G. K. (2016). Electrocardiogram signal compression using singular coefficient truncation and wavelet coefficient coding. IET Science, Measurement & Technology, 10, 1–9.Google Scholar
  106. 106.
    Islam, K. R., Abedin, M. A., Akter, M., & Deb, R. (2011). High speed ECG image compression using modified SPIHT. International Journal of Computer and Electrical Engineering, 3(3), 1–5.Google Scholar
  107. 107.
    Mohammadpour, T. I. (2009). ECG compression with thresholding of 2-D wavelet transform coefficients and run length coding. European Journal of Scientific Research, 27(2), 248–257.Google Scholar
  108. 108.
    Wheeler, F. W., & Pearlman, W. A. (2000). SPIHT image compression without lists. In 2000 IEEE international conference on acoustic speech, signal process. proceedings, (Vol. 6, pp. 2047–2050).Google Scholar
  109. 109.
    Pooyan, M., Taheri, A., Moazami-goudarzi, M., Saboori, I., & Introduction, A. (2005). Wavelet compression of ECG signals using SPIHT algorithm. World Academy of Sciences Engineering and Technology, 2(3), 212–215.Google Scholar
  110. 110.
    Rajankar, S., Bhanushali, R., & Talbar, S. (2016). A wavelet-based progressive ECG compression using modified SPIHT. International Journal of Biomedical Engineering and Technology, 22(3), 216–232.Google Scholar
  111. 111.
    Rajankar, S., & Talbar, S. (2016). A quality-on-demand electrocardiogram signal compression using modified set partitioning in hierarchical tree. Signal, Image Video Processing, 10(8), 1559–1566.Google Scholar
  112. 112.
    Awal, M. A., Mostafa, S. S., Ahmad, M., & Rashid, M. A. (2014). An adaptive level dependent wavelet thresholding for ECG denoising. Biocybernetics and Biomedical Engineering, 34(4), 238–249.Google Scholar
  113. 113.
    Ramakrishnan, A. G., & Saha, S. (1997). ECG coding by wavelet-based linear prediction. IEEE Transactions on Biomedical Engineering, 44(12), 1253–1261.Google Scholar
  114. 114.
    Kumar, R., Kumar, A., & Singh, G. K. (2016). Electrocardiogram signal compression based on 2D-transforms: A research overview. Journal of Medical Imaging and Health Informatics, 6(2), 285–296.Google Scholar

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Authors and Affiliations

  1. 1.Sinhgad College of EngineeringPuneIndia
  2. 2.Shri Guru Gobind Singhji Institute of Engineering and TechnologyVishnupuri, NandedIndia

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