http://iet.metastore.ingenta.com
1887

Efficient compression technique based on temporal modelling of ECG signal using principle component analysis

Efficient compression technique based on temporal modelling of ECG signal using principle component analysis

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Science, Measurement & Technology — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

This study presents an improved technique for compression of electrocardiogram (ECG) signals, based on beat correlation of signal and principle component (PC) analysis, for ECG signal. For this purpose, two-dimensional matrix of ECG signal based on temporal inter-and intra-beat correlation is constructed, and further compression is achieved using PC extraction. Beat correlation helps to generate very few PCs that increase the compression efficiency. A detailed analysis has been presented for ten signals having different rhythms, wave morphologies and abnormalities of Massachusetts Institute of Technology - Beth Israel Hospital (MIT-BIH) arrhythmia database. The effectiveness of the proposed method is examined with several attributes such as percentage root-mean-square difference, compression ratio, signal-to-noise ratio and correlation. Experimental results have shown that this method is very efficient for compression and suitable for different applications of telecardiology.

References

    1. 1)
      • 1. Kumar, R., Kumar, A., Singh, G.K.: ‘Electrocardiogram signal compression based on 2D-transforms: a research overview’, J. Med. Imaging Health Inf., 2016, 6, (2), pp. 285396.
    2. 2)
      • 2. Manikandan, M.S., Dandapat, S.: ‘Wavelet-based electrocardiogram signal compression methods and their performances: a prospective review’, Biomed. Signal Proc. Control, 2014, 14, pp. 73107.
    3. 3)
      • 3. Pattichis, M.S.: ‘Signal, image and video compression for m-health applications’, in Istepanian, R., Laxminarayan, S., Pattichis, C.S. (EDs.): ‘M-health: emerging mobile health systems’ (Springer, USA, 2006).
    4. 4)
      • 4. Raju, P.K., Prasad, S.G.: ‘Telemedicine and cardiology-decade of our experience’, J. Indian Coll. Cardiol., 2012, 2, (1), pp. 416.
    5. 5)
      • 5. Tompkins, W.J.: ‘Electrocardiography’, in Tompkins, W.J. (EDs.): ‘Biomedical digital signal processing’ (Prentice-Hall, Upper Saddle River, NJ, USA, 1999), Ch. 2.
    6. 6)
      • 6. Reddy, D.C.: ‘Cardiological signal processing’, in Reddy, D.C. (EDs.): ‘Biomedical signal processing: principles and techniques’ (Tata McGraw-Hills Education Pvt. Ltd., 2011), Ch. 7.
    7. 7)
      • 7. Jalaleddine, S.M.S., Hutchens, C.G., Stratton, R.D., et al: ‘ECG data compression techniques – a unified approach’, IEEE Trans. Biomed. Eng., 1999, 37, (4), pp. 329343.
    8. 8)
      • 8. Kumar, R., Kumar, A., Pandey, R.K.: ‘Beta wavelet based ECG signal compression using lossless encoding with modified thresholding’, Comput. Electr. Eng., 2013, 39, (1), pp. 130140.
    9. 9)
      • 9. Schooley, B., Abed, Y., Murad, A., et al: ‘Design and field test of a M-health system for emergency medical services’, Health Technol., 2013, 3, pp. 327340.
    10. 10)
      • 10. Manikandan, M.S., Dandapat, S.: ‘Wavelet threshold based ECG compression using USZZQ and Huffman coding of DSM’, Biomed. Signal Proc. Control, 2006, 1, (4), pp. 261270.
    11. 11)
      • 11. Lee, H., Buckley, K.M.: ‘ECG data compression using cut and align beats approach and 2-D transforms’, IEEE Trans. Biomed. Eng., 1999, 46, (5), pp. 556564.
    12. 12)
      • 12. Alexandre, E., Pena, A., Sobreira, M.: ‘On the use of 2-D coding techniques for ECG signals’, IEEE Trans. Inf. Technol. Biomed., 2006, 10, (4), pp. 809811.
    13. 13)
      • 13. Lukin, V., Zriakhov, M., Zelenskt, A.A., et al: ‘Lossy compression of multichannel ECG based on 2-D DCT and pre-processing’. TCSET'2008, 2008, pp. 159162.
    14. 14)
      • 14. Sahraeian, S.M.E., Fatemizadeh, E.: ‘Wavelet-based 2-D ECG data compression method using SPIHT and VQ coding’. EUROCON'2007, 2007, pp. 133137.
    15. 15)
      • 15. Huang, B., Wang, Y.: ‘2-D compression of ECG signals using ROI mask and conditional entropy coding’, IEEE Trans. Biomed. Eng., 2009, 56, (4), pp. 12611263.
    16. 16)
      • 16. Lu, Z., Kim, D.Y., Pearlman, W.A.: ‘Wavelet compression of ECG signals by the set partitioning in hierarchical trees algorithm’, IEEE Trans. Biomed. Eng., 2000, 47, pp. 849856.
    17. 17)
      • 17. Tai, S.Ch., Sun, Ch.Ch., Yan, W.Ch.: ‘A 2-D ECG compression method based on wavelet transform and modified SPIHT’, IEEE Trans. Biomed. Eng., 2005, 52, (6), pp. 9991008.
    18. 18)
      • 18. Wei, J.-J., Chang, Ch.-J., Chou, N.-K., et al: ‘ECG data compression using truncated singular value decomposition’, IEEE Trans. Inf. Technol. Biomed., 2001, 5, (4), pp. 290299.
    19. 19)
      • 19. Padhy, S., Sharma, L.N., Dandapat, S.: ‘Multilead ECG compression using SVD in multiresolution domain’, Biomed. Signal Proc. Control, 2016, 23, pp. 1018.
    20. 20)
      • 20. Pan, J., Tompkins, W.J.: ‘A real-time QRS detection algorithm’, IEEE Trans. Biomed. Eng., 1985, 32, (3), pp. 230236.
    21. 21)
      • 21. Castells, F., Laguna, P., Sornmo, L., et al: ‘Principle component analysis in ECG signal processing’, EURASIP J. Adv. Signal Process., 2007, 1, pp. 98119.
    22. 22)
      • 22. Sharma, L.N., Dandapat, S., Mahanta, A.: ‘Multichannel ECG data compression based on multiscale principle component analysis’, IEEE Trans. Inf. Technol. Biomed., 2012, 16, (4), pp. 730736.
    23. 23)
      • 23. Ranjeet, K., Kumar, A., Pandey, R.K.: ‘An efficient compression system for ECG signal using QRS periods and CAB technique based on 2D DWT and Huffman coding’. IEEE Int. Conf. CARE 2013, 2013, pp. 16.
    24. 24)
      • 24. Kumar, R., Kumar, A., Singh, G.K.: ‘ECG signal compression using singular coefficient truncation and wavelet coefficient coding’, IET Sci. Meas. Technol., 2016, 10, (4), pp. 266274.
    25. 25)
      • 25. Kumar, R., Kumar, A., Singh, G.K.: ‘Hybrid method based on singular value decomposition and embedded zero tree wavelet technique for ECG signal compression’, Comput. Methods Programs Biomed., 2016, 129, pp. 135139.
    26. 26)
      • 26. Kumar, R., Kumar, A., Singh, G.K.: ‘Electrocardiogram signal compression based on singular value decomposition (SVD) and adaptive scanning wavelet difference reduction (ASWDR) technique’, Int. J. Electron. Commun., 2015, 69, (12), pp. 18101822.
    27. 27)
      • 27. Prashad, K.P., Satyanarayana, P.: ‘Fast interpolation algorithm using FFT’, IEEE Electron. Lett., 1986, 22, (4), pp. 185187.
    28. 28)
      • 28. Chawla, M.P.S.: ‘A comparative analysis of principal component and independent component techniques for electrocardiograms’, Neural Comput. Appl., 2009, 18, (6), pp. 539556.
    29. 29)
      • 29. Chawla, M.P.S.: ‘PCA and ICA processing methods for removal of artifacts and noise in electrocardiograms: a survey and comparison’, Appl. Soft Comput., 2011, 11, (2), pp. 22162226.
    30. 30)
      • 30. MIT-BIH Arrhythmia database. Available at http://www.physionet.org/physiobank/database/mitdb.
    31. 31)
      • 31. Zigel, Y., Cohen, A., Katz, A.: ‘The weighted diagnostic distortion (WDD) measure for ECG signal compression’, IEEE Trans. Biomed. Eng., 2000, 47, (11), pp. 14221430.
    32. 32)
      • 32. Kumar, A., Ranjeet, K.: ‘ECG signal compression using optimized wavelet filter bank’, Int. J. Signal Imaging Syst. Eng., 2012, 5, (3), pp. 187195.
    33. 33)
      • 33. Ranjeet, K., Kumar, A., Pandey, R.K.: ‘ECG signal compression using optimum wavelet filter bank based on Kaiser window’, Procedia Eng., 2012, 38, pp. 28892902.
    34. 34)
      • 34. Ranjeet, K., Kumar, A., Pandey, R.K.: ‘ECG signal compression using different techniques’, Commun. Comput. Inf. Sci., 2011, 125, (2), pp. 231241.
    35. 35)
      • 35. Kumar, A., Ranjeet, K.: ‘Wavelet based electrocardiogram compression at different quantization levels’, Commun. Comput. Inf. Sci., 2011, 147, (3), pp. 392398.
    36. 36)
      • 36. Craven, D., McGinley, B., Kilmartin, L., et al: ‘Energy-efficient compresses sensing for ambulatory ECG monitoring’, Comput. Biol. Med., 2016, 71, pp. 113.
    37. 37)
      • 37. Polania, L.F., Carrillo, R.E., Blanco-Velasco, M., et al: ‘Exploiting prior knowledge in compressed sensing wireless ECG systems’, IEEE J. Biomed. Health Inf., 2015, 19, (2), pp. 508519.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-smt.2016.0360
Loading

Related content

content/journals/10.1049/iet-smt.2016.0360
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address