COHERENCY AND SHARPNESS MEASURES BY USING ICA ALGORITHMS - An Investigation for Alzheimer's Disease Discrimination

Jordi Solé-Casals, François Vialatte, Zhe Chen, Andrzej Cichocki

Abstract

In this paper, we present a comprehensive study of different Independent Component Analysis (ICA) algorithms for the calculation of coherency and sharpness of electroencephalogram (EEG) signals, in order to investigate the possibility of early detection of Alzheimer's disease (AD). We found that ICA algorithms can help in the artifact rejection and noise reduction, improving the discriminative property of features in high frequency bands (specially in high alpha and beta ranges). In addition to different ICA algorithms, the optimum number of selected components is investigated, in order to help decision processes for future works.

References

  1. Alexander, G. E. (2002). Longitudinal pet evaluation of cerebral metabolic decline in dementia: A potential outcome measure in alzheimer's disease treatment studies. In American Journal of Psychiatry, vol. 159, pp. 738-745.
  2. Andreasen, N., Minthon, L., Davidsson, P., Vanmechelen, E., and et al. (2001). Evaluation of csf-tau and csf-a?2 as diagnostic markers for alzheimer disease in clinical practice. In Am Med Assoc, vol. 58, pp. 373-379.
  3. Babiloni, C., Ferri, R., Binetti, G., Cassarino, A., Forno, G. D., Eercolani, M., Ferreri, F., Frisoni, G., and et al. (2006). Fronto-parietal coupling of brain rhythms in mild cognitive impairment: A multicentric eeg study. In Brain Research Bulletin, pp. 63-67.
  4. Belouchrani, A., Abed-Meraim, K., Cardoso, J.-F., and Moulines, E. (1997). A blind source separation technique using second-order statistics. In IEEE Trans. Signal Processing, vol. 45, pp. 434-444.
  5. Borga, M. and Knutsson, H. (2001). A canonical correlation approach to blind source separation. In Technical Report LiU-IMT-EX-0062, Department of Biomedical Engineering.
  6. Cardoso, J. F. and Souloumiac, A. (1993). Blind beamforming for non-gaussian signals. In IEE Proceedings - Part F, 140, 362-370.
  7. Cichocki, A. and Amari, S. (2002). Adaptive Blind Signal and Image Processing. Wiley, New York.
  8. Cichocki, A., Shishkin, S. L., Musha, T., Leonowicz, Z., Asada, T., and Kurachi, T. (2005). Eeg filtering based on blind source separation (bss) for early detection of alzheimer's disease. In Clinical Neurophysiology, 116, pp. 729-737.
  9. Cruces-Alvarez, S. A., Cichocki, A., and Lathauwer, L. D. (2004). Thin qr and svd factorizations for simultaneous blind signal extraction. In Proc. European Signal Processing Conference (EUSIPCO), Vienna, Austria, pp. 217-220.
  10. Delorme, A., Makeig, S., and Sejnowski, T. (2001). Automatic artifact rejection for eeg data using high-order statistics and independent component analysis. In 3rd ICASSP International Workshop, San Diego,.
  11. Deweer, B., Lehericy, S., Pillon, B., Baulac, M., and et al. (1995). Memory disorders in probable alzheimer's disease: the role of hippocampal atrophy as shown with mri. In British Medical Journal, vol. 58, p. 590.
  12. Ferri, C. P., Prince, M., Brayne, C., and et al., H. B. (2006). Global prevalence of dementia: a delphi consensus study. In The Lancet, vol. 366, pp. 2112-2117.
  13. Févotte, C. and Doncarli, C. (2004). Two contributions to blind source separation using time-frequency distributions. In IEEE Signal Processing Letters, 11, pp. 386- 389.
  14. Gonzalez, R. and Woods, R. (1992). Digital Image Processing. Addison-Wesley.
  15. Hyvarinen, A. and Oja, E. (1997). A fast fixed-point algorithm for independent component analysis. In Neural Computation, 9(7) pp. 1483-1492.
  16. Karvanen, J., Eriksson, J., and Koivunen, V. (2000). Pearson system based method for blind separation. In Workshop on Independent Component Analysis and Blind Signal Separation, ICA2000, Helsinki, pp. 585- 590.
  17. Kenney, J. F. and Keeping, E. S. (1962). Mathematics of Statistics. Part 1. Van Nostrand, Princeton, NJ.
  18. Koenig, T., Prichep, L., Dierks, T., Hubl, D., Wahlund, L., John, E., and Jelic., V. (2005). Decreased eeg synchronization in alzheimer's disease and mild cognitive impairment. In Neurobiology of Aging, 26, pp. 165-171.
  19. Solé-Casals, J., Vialatte, F., and Cichocki, Z. C. A. (2008). Investigation of ica algirithms for feature extraction of eeg signals in discrimination of alzheimer disease. In Proc. International Conference on Bio-Inspired Systems and Signal Processing, Biosignals, pp. 232-235.
  20. Tanzi, R. E. and Bertram, L. (2001). New frontiers in alzheimer's disease genetics. In Neuron, vol. 32, pp. 181-184.
  21. Tong, L., Soon, V., Huang, Y. F., and Liu, R. (1991). Indeterminacy and identifiability of blind identification. In IEEE Trans. CAS, vole. 38, pp. 499-509.
  22. Vialatte, F., Cichocki, A., Dreyfus, G., Musha, T., Rutkowski, T., and Gervais, R. (2005). Blind source separation and sparse bump modelling of time frequency representation of eeg signals: New tools for early detection of alzheimer's disease. In Proc. IEEE Workshop on Machine Learning for Signal Processing, pp. 27-32.
Download


Paper Citation


in Harvard Style

Solé-Casals J., Vialatte F., Chen Z. and Cichocki A. (2009). COHERENCY AND SHARPNESS MEASURES BY USING ICA ALGORITHMS - An Investigation for Alzheimer's Disease Discrimination . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2009) ISBN 978-989-8111-65-4, pages 468-475. DOI: 10.5220/0001430904680475


in Bibtex Style

@conference{biosignals09,
author={Jordi Solé-Casals and François Vialatte and Zhe Chen and Andrzej Cichocki},
title={COHERENCY AND SHARPNESS MEASURES BY USING ICA ALGORITHMS - An Investigation for Alzheimer's Disease Discrimination},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2009)},
year={2009},
pages={468-475},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001430904680475},
isbn={978-989-8111-65-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2009)
TI - COHERENCY AND SHARPNESS MEASURES BY USING ICA ALGORITHMS - An Investigation for Alzheimer's Disease Discrimination
SN - 978-989-8111-65-4
AU - Solé-Casals J.
AU - Vialatte F.
AU - Chen Z.
AU - Cichocki A.
PY - 2009
SP - 468
EP - 475
DO - 10.5220/0001430904680475