A High Performance Scheme for EEG Compression Using a Multichannel Model

  • D. Gopikrishna
  • Anamitra Makur
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2552)

Abstract

The amount of data contained in electroencephalogram (EEG) recordings is quite massive and this places constraints on bandwidth and storage. The requirement of online transmission of data needs a scheme that allows higher performance with lower computation. Single channel algorithms, when applied on multichannel EEG data fail to meet this requirement. While there have been many methods proposed for multichannel ECG compression, not much work appears to have been done in the area of multichannel EEG compression. In this paper, we present an EEG compression algorithm based on a multichannel model, which gives higher performance compared to other algorithms. Simulations have been performed on both normal and pathological EEG data and it is observed that a high compression ratio with very large SNR is obtained in both cases. The reconstructed signals are found to match the original signals very closely, thus confirming that diagnostic information is being preserved during transmission.

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References

  1. [1]
    EEG data compression techniques, Antoniol, G.; Tonella, P., IEEE Transactions on Biomedical Engineering, Volume: 44 Issue: 2, Feb. 1997, Page(s): 105–114Google Scholar
  2. [2]
    Recurrent neural network predictors for EEG signal compression, Bartolini, F.; Cappellini, V.; Nerozzi, S.; Mecocci, A., International Conference on Acoustics, Speech, and Signal Processing, 1995. ICASSP-95 Volume: 5, 1995 Page(s): 3395–3398.Google Scholar
  3. [3]
    Lossless and near-lossless compression of EEG signals, Cinkler, J.; Kong, X., Memon, N.,Conference Record of the Thirty-First Asilomar Conference on Signals, Systems & Computers.Volume: 2, 1997, Page(s): 1432–1436.CrossRefGoogle Scholar
  4. [4]
    Tree structured filter bank for time-frequency decomposition of EEG signals, Sijercic, Z.; Agarwal, G., IEEE 17th Annual Conference Engineering in Medicine and Biology Society 1995, Volume: 2, 1995, Page(s): 991–992.Google Scholar
  5. [5]
    EEG signal compression with ADPCM subband coding, Sijercic, Z.; Agarwal, G.C.; Anderson, C.W., IEEE 39th Midwest symposium on Circuits and Systems, Volume: 2, 1996, Page(s): 695–698.CrossRefGoogle Scholar
  6. [6]
    Use of chaotic modeling for transmission of EEG data, Kavitha, V.; Narayana Dutt, D., ICICS Proceedings of 1997 International Conference on Information, Communications and Signal Processing, 1997. Volume: 3, 1997, Page(s): 1262–1265.CrossRefGoogle Scholar
  7. [7]
    Spatio-temporal EEG information transfer in an episode of epilepsy, A.M. Albano et al Nonlinear Dynamics and Brain Functioning (eds.-N. Pradhan, P.E. Rapp and R. Sreenivasan), Nova Science Publishers, Newyork, 199, Page(s): 411–434.Google Scholar
  8. [8]
    Entropy of brain rhythms: normal versus injury EEG, Thakor, N.V.; Paul, J.; Tong, S.; Zhu, Y.; Bezerianos, A., Proceedings of the 11th IEEE Signal Processing Workshop on Statistical Signal Processing, 2001, Page(s): 261–264Google Scholar
  9. [9]
    A lossless compression algorithm for multichannel EEG, Ke Chu Yi; Mingui Sun; Ching Chung Li; Sclabassi, R.J., Proceedings of the First Joint BMES/EMBS Conference, 1999. Volume: 1, 1999, Page(s): 429CrossRefGoogle Scholar
  10. [10]
    Long-term EEG compression for intensive-care settings, Agarwal, R.; Gotman, J. IEEE Engineering in Medicine and Biology Magazine, Volume: 20 Issue: 5, Sept.-Oct. 2001, Page(s): 23–29CrossRefGoogle Scholar
  11. [11]
    Vector quantization for compression of multichannel ECG, Mammen, C.P.; Ramamurthi, B., IEEE Transactions on Biomedical Engineering, Volume: 37 Issue:9, Sept. 1990, Page(s): 821–825.CrossRefGoogle Scholar
  12. [12]
    Multichannel ECG data compression by multirate signal processing and transform domain coding techniques, Cetin, A.E.; Koymen, H.; Aydin, M.C., IEEE Transactions on Biomedical Engineering, Volume: 40 Issue:5, May 1993,Page(s): 495–499CrossRefGoogle Scholar
  13. [13]
    A multichannel template based data compression algorithm, Paggetti, C.; Lusini, M.; Varanini, M.; Taddei, A.; Marchesi, C., Computers in Cardiology 1994, Page(s): 629–632Google Scholar
  14. [14]
    Compression of multichannel ECG through multichannel long-term prediction Cohen, A.; Zigel, Y., IEEE Engineering in Medicine and Biology Magazine, Volume: 17 Issue:1, Jan.-Feb. 1998, Page(s): 109–115CrossRefGoogle Scholar
  15. [15]
    Near-best WPT compression of polysomnograms, Niederholz, J.; Taswell, C., Proceedings of the First Joint BMES/EMBS Conference, Volume: 2, 1999, Page(s): 961.CrossRefGoogle Scholar
  16. [16]
    Quality controlled compression of polysomnograms, Taswell, C.; Niederholz, J., Proceedings of the First Joint BMES/EMBS Conference, 1999, Volume: 2, 1999, Page(s): 944.CrossRefGoogle Scholar
  17. [17]
    Multichannel ECG compression using multichannel adaptive vector quantization, Shaou-Gang Miaou; Heng-Lin Yen, IEEE Transactions on Biomedical Engineering, Volume: 48 Issue: 10, Oct. 2001, Page(s): 1203–1207CrossRefGoogle Scholar
  18. [18]
    Multichannel ECG data compression method based on a new modeling method Prieto, A.; Mailhes, C., Computers in Cardiology, 2001, Page(s): 261–264Google Scholar
  19. [19]
    Gilbert Strang and Truong Ngugen.: Wavelets and Filterbanks, Cambridge University Press, 1996.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • D. Gopikrishna
    • 1
  • Anamitra Makur
    • 1
  1. 1.Dept. of Electrical Communication EngineeringIndian Institute of ScienceBangaloreIndia

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