Skip to main content

Advertisement

SpringerLink
Log in

Quality Aware Compression of Electrocardiogram Using Principal Component Analysis

  • Systems-Level Quality Improvement
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

Electrocardiogram (ECG) compression finds wide application in various patient monitoring purposes. Quality control in ECG compression ensures reconstruction quality and its clinical acceptance for diagnostic decision making. In this paper, a quality aware compression method of single lead ECG is described using principal component analysis (PCA). After pre-processing, beat extraction and PCA decomposition, two independent quality criteria, namely, bit rate control (BRC) or error control (EC) criteria were set to select optimal principal components, eigenvectors and their quantization level to achieve desired bit rate or error measure. The selected principal components and eigenvectors were finally compressed using a modified delta and Huffman encoder. The algorithms were validated with 32 sets of MIT Arrhythmia data and 60 normal and 30 sets of diagnostic ECG data from PTB Diagnostic ECG data ptbdb, all at 1 kHz sampling. For BRC with a CR threshold of 40, an average Compression Ratio (CR), percentage root mean squared difference normalized (PRDN) and maximum absolute error (MAE) of 50.74, 16.22 and 0.243 mV respectively were obtained. For EC with an upper limit of 5 % PRDN and 0.1 mV MAE, the average CR, PRDN and MAE of 9.48, 4.13 and 0.049 mV respectively were obtained. For mitdb data 117, the reconstruction quality could be preserved up to CR of 68.96 by extending the BRC threshold. The proposed method yields better results than recently published works on quality controlled ECG compression.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Jalaleddine, S. M. S., Hutchens, C. G., and Strattan, R. D., ECG data compression techniques-a unified approach. IEEE Trans. Biomed. Eng. 37(4):329–343, 1990.

    Article  CAS  PubMed  Google Scholar 

  2. Cox, J. R., Nolle, F. M., Fozzard, H. A., Oliver, G. C., and AZTEC, A preprocessing program for real-time ECG rhythm analysis. IEEE Trans. Biomed. Eng. BME-15:128–129, 1968.

    Article  Google Scholar 

  3. Muller, W. C., Arrhythmia detection program for an ambulatory ECG monitor. Biomed Sci Instrum 14:81–85, 1978.

    Google Scholar 

  4. Abenstein, J. P., and Tompkins, W. J., New data-reduction algorithm for real-time ECG analysis. IEEE Trans. Biomed. Eng. BME-29:43–48, 1982.

    Article  Google Scholar 

  5. Pollard, A. E., and Barr, R. C., Adaptive sampling of intracellular and extracellular cardiac potentials with the fan method. Med. Biol. Eng. Comput. 25(3):261–268, 1987.

    Article  CAS  PubMed  Google Scholar 

  6. Barr, R. C., Blanchard, S. M., and Dipersio, D. A., SAPA-2 is fan. IEEE Trans. Biomed. Eng. BME-32(5):337, 1985.

    Article  Google Scholar 

  7. Cortman, C. M., Data compression by redundancy reduction. Proc. IEEE 133–139, 1965.

  8. Steward, D., Dower, G. E., and Suranyi, O., An ECG compression code. J. Electrocardiol. 6(2):175–176, 1973.

    Article  Google Scholar 

  9. Gupta, R., and Mitra, M., Wireless electrocardiogram transmission in ISM band: an approach towards telecardiology. J. Med. Syst. 38(10):1–14, 2014.

    Article  Google Scholar 

  10. Roy, S., and Gupta, R., Short range centralized cardiac health monitoring system based on zigbee communication. Proc IEEE Global Humanitarian Technology Conference (GHTC)-South Asia Satellite (SAS), 26–27 September, 2014, Kerala, India, pp. 177–182.

  11. Hamilton, P. S., and Tompkins, W. J., Compression of the ambulatory ECG by average beat subtraction and residual differencing. IEEE Trans. Biomed. Eng. 38(3):253–259, 1991.

    Article  CAS  PubMed  Google Scholar 

  12. Mammen, C. P., and Ramamurthi, B., Vector quantization for compression of multi-channel ECG. IEEE Trans. Biomed. Eng. 37(9):821–825, 1990.

    Article  CAS  PubMed  Google Scholar 

  13. Dutt, D. N., Krishnan, S. M., and Srinivasan, N., A dynamic nonlinear time domain model for reconstruction and compression of cardiovascular signals with application to telemedicine. Comput. Biol. Med. 33:45–63, 2003.

    Article  PubMed  Google Scholar 

  14. Al-Nashash, H. A. M., A dynamic Fourier series for the compression of ECG using FTT and adaptive coefficient. Med. Eng. Phys. 17(3):197–203, 1995.

    Article  CAS  PubMed  Google Scholar 

  15. Batista, L. V., Melcher, E. U. K., and Carvalho, L. C., Compression of ECG Signals by optimized quantization of discrete cosine transform coefficients. Med. Eng. Phys. 23(2):127–134, 2001.

    Article  CAS  PubMed  Google Scholar 

  16. Colomer, A. A., Adaptive ECG data compression using discrete legendre transform. Digital Signal Process. 7(4):222–228, 1997.

    Article  Google Scholar 

  17. Degani, R., Bortolan, G., and Murolo, S., Karhunen Louve coding of ECG signals. Proc Computers in Cardiology, September 23–26, 1990, Chicago, pp. 395–398.

  18. Blanchett, T., Kember, G. C., and Fenton, G. A., KLT-based quality controlled compression of single-lead ECG. IEEE Trans. Biomed. Eng. 45(7):942–945, 1998.

    Article  CAS  PubMed  Google Scholar 

  19. Xingyuan, W., and Juan, M., Wavelet based hybrid ECG compression technique. Analog Integr. Circ. Sig. Proccess. 59(3):301–308, 2009.

    Article  Google Scholar 

  20. Chen, J., Yang, M., Zhang, Y., and Shi, X., ECG compression by optimized quantization of wavelet coefficients. Intell. Comput. Signal Process. Pattern Recogn. LNCIS 345:809–814, 2006.

    Article  Google Scholar 

  21. Istepanian, R. S. H., Hadjileontiadis, L. J., and Panas, S. M., ECG data compression using wavelets and higher order statistics methods. IEEE Trans. Inf. Technol. Biomed. 5(2):108–115, 2001.

    Article  CAS  PubMed  Google Scholar 

  22. Kim, B. S., Yoo, S. K., and Lee, M. H., Wavelet-based low delay ECG compression algorithm for continuous ECG transmission. IEEE Trans. Biomed. Eng. 10(1):77–83, 2006.

    Article  Google Scholar 

  23. Manikandan, M. S., and Dandapat, S., Wavelet-based electrocardiogram signal compression methods and their performances: a prospective review. Biomed. Signal Process. Control 14:73–107, 2014.

    Article  Google Scholar 

  24. Jigel, Y., Cohen, A., and Katz, A., The weighted diagnostic distortion measure for ECG signal compression. IEEE Trans. Biomed. Eng. 47(11):1422–1430, 2000.

    Article  Google Scholar 

  25. Al-Fahoum, A. S., Quality assessment of ECG compression techniques using a wavelet-based diagnostic measure. IEEE Trans. Inf. Technol. Biomed. 10(1):182–191, 2006.

    Article  PubMed  Google Scholar 

  26. Ku, C. T., Hung, K. C., Wu, T. C., and Wang, H. S., Wavelet based ECG data compression system with linear quality control scheme. IEEE Trans. Biomed. Eng. 57(6):1399–1409, 2010.

    Article  PubMed  Google Scholar 

  27. Alesanco, A., and Garcia, J., Automatic real-time ECG coding methodology guaranteeing signal interpretation quality. IEEE Trans. Biomed. Eng. 55(11):2519–2527, 2008.

    Article  PubMed  Google Scholar 

  28. Physionet data: http://www.physionet.org.

  29. Banerjee, S., Gupta, R., and Mitra, M., Delineation of ECG characteristic features using multiresolution wavelet analysis method. Measurement 45(3):474–487, 2012.

    Article  Google Scholar 

  30. Jollife, I. T., Principal component analysis. Springer, New York, 2002.

    Google Scholar 

  31. Gupta, R., and Mitra, M., An ECG compression technique for telecardiology application. Proc IEEE India Conf (INDICON), December 16–18, 2011, Hyderabad, India, pp. 1–4.

  32. Gupta, R., Lossless compression technique for real time photoplethysmographic measurements. IEEE Trans. Instrum. Meas. 64(4):975–983, 2015.

    Article  Google Scholar 

  33. Morris, F., Brady, W. J., and Camm, J. (Eds.), ABC of clinical cardiography, 2nd edition. Blackwell, USA, 2008.

    Google Scholar 

  34. Lee, S., Kim, J., and Lee, M., A real-time ECG data compression and transmission algorithm for an e-health device. IEEE Trans. Biomed. Eng. 58(9):2448–2455, 2011.

    Article  PubMed  Google Scholar 

  35. Mamaghanian, H., Khaled, N., Atienza, D., and Vandergheynst, P., Compressed sensing for real-time energy-efficient ECG compression on wireless body sensor nodes. IEEE Trans. Biomed. Eng. 58(9):2456–2466, 2011.

    Article  PubMed  Google Scholar 

  36. Benzid, R., Marir, F., Boussaad, A., Benyoucef, M., and Arar, D., Fixed percentage of wavelet coefficients to be zeroed for ECG compression. Electron. Lett. 39(11):830–831, 2003.

    Article  Google Scholar 

  37. Kim, H., Yazicioglu, R. F., Merken, P., Hoof, C. V., and Yoo, H. J., ECG signal compression and classification algorithm with quad level vector for ECG holter system. IEEE Trans. Biomed. Eng. 14(1):93–100, 2010.

    Article  Google Scholar 

  38. Mitra, M., Bera, J. N., and Gupta, R., Electrocardiogram compression technique for global system of mobile-based offline telecardiology application for rural clinics in India. IET Sci. Meas. Tech. 6(6):412–419, 2012.

    Article  Google Scholar 

  39. Ma, J. L., Zhang, T. T., and Dong, M. C., A novel ECG data compression method using adaptive fourier decomposition with security guarantee in e-health applications. IEE J. Biomed. Health Inf. 19(3):986–994, 2015.

    Google Scholar 

Download references

Acknowledgments

The author extends sincere thanks to Dr. Jayanta Saha, MD (Medicine), DM (Cardiology), and Dr. Supratip Kundu, MD (Medicine), of Calcutta Medical College & Hospital, Kolkata, India for carrying out the clinical evaluation of reconstructed data through double blind tests. The author also acknowledges SAP DRS II program 2015–2020 at Dept of Applied Physics, University of Calcutta and Department of Science & Technology, Govt. of West Bengal, India.

Author information

Authors and Affiliations

  1. Instrumentation Engineering Section, Department of Applied Physics, University of Calcutta, 92 APC Road, Kolkata, India, 700009

    Rajarshi Gupta

Authors
  1. Rajarshi Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rajarshi Gupta.

Additional information

This article is part of the Topical Collection on Systems-Level Quality Improvement

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gupta, R. Quality Aware Compression of Electrocardiogram Using Principal Component Analysis. J Med Syst 40, 112 (2016). https://doi.org/10.1007/s10916-016-0468-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-016-0468-7

Keywords

Search

Navigation