Skip to main content

EEG Complexity Modifications and Altered Compressibility in Mild Cognitive Impairment and Alzheimer’s Disease

  • Conference paper
Recent Advances of Neural Network Models and Applications

Abstract

The objective of this work is to respond to the question: can quantitative electroencephalography (EEG) distinguish among Alzheimer’s Disease (AD) patients, mild cognitive impaired (MCI) subjects and elderly healthy controls? In other words, are there nonlinear indexes extracted from raw EEG data that are able to manifest the background difference among EEG? The response we give here is that a synthetic index of entropic complexity (Permutation Entropy, PE) as well as a measure of compressibility of the EEG can be used to discriminate among classes of subjects. An experimental database has been analyzed to make these measurements and the results we achieved are encouraging also in terms of disease evolution. Indeed, it is clearly shown that the condition of MCI has intermediate properties with respect to the analyzed markers: thus, these markers could in principle be used to evaluate the probability of transition from MCI to mild AD.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Weiner, M.W., et al.: The Alzheimer’s Disease Neuroimaging Iniziative: A review. Alzheimer’s & Dementia 8, S1–S68 (2012)

    Google Scholar 

  2. Jeong, J.: EEG Dynamics in patients with Alzheimer’s disease. Clinical Neurophysiology 115, 1490–1505 (2004)

    Article  Google Scholar 

  3. Bandt, C., Pompe, B.: Permutation entropy - a natural complexity measure for time series. Physical Review Lett. 88, 174102 (2002)

    Article  Google Scholar 

  4. Dauwels, J., Srinivasan, K., et al.: Slowing and loss of complexity in Alzheimer’s EEG: two sides of the same coin? Intl. J. of Alzheimer’s Disease (2011)

    Google Scholar 

  5. Labate, D., La Foresta, F., Morabito, G., Palamara, I., Morabito, F.C.: Entropic Measures of EEG Complexity in Alzheimer’s Disease through a Multivariate Multiscale Approach. IEEE Sensors Journal 13(9), 3284–3292 (2013)

    Article  Google Scholar 

  6. Morabito, F.C., Labate, D., Bramanti, A., La Foresta, F., Morabito, G., Palamara, I., Szu, H.H.: Enhanced Compressibility of EEG Signal in Alzheimer’s Disease Patients. IEEE Sensors Journal 13(9), 3255–3262 (2013)

    Article  Google Scholar 

  7. Zhang, Z., Jung, T.P., Makeig, S., Rao, B.D.: Compressed Sensing of EEG for Wireless Telemonitoring with Low Energy Consumption and Inexpensive Hardware. IEEE Trans. Biomed. Eng. 60(1), 221–224 (2013)

    Article  Google Scholar 

  8. Cands, E.J., Wakin, M.B.: An introduction to compressive sensing. IEEE Signal Processing Magazine 25(2), 14–20 (2008)

    Google Scholar 

  9. Donoho, D.: Compressed sensing. IEEE Trans. Inform Theory 52(4), 1289–1306 (2006)

    Article  MathSciNet  Google Scholar 

  10. Zhang, Z., Rao, B.D.: Extension of SBL algorithms for the recovery of block sparse signals with intra-block correlation. IEEE Trans. on Signal Processing 61(8), 2009–2015 (2013)

    Article  Google Scholar 

  11. Tipping, M.: Sparse Bayesian learning and the relevance vector machine. Journal of Machine Learning Research 1, 211–244 (2001)

    MATH  MathSciNet  Google Scholar 

  12. Morabito, F.C., Labate, D., La Foresta, F., Bramanti, A., Morabito, G., Palamara, I.: Multivariate Multi-Scale Permutation Entropy for Complexity Analysis of Alzheimer’s Disease EEG. Entropy 14(7), 1186–1202 (2012)

    Article  Google Scholar 

  13. Keller, K., Sinn, K.: Ordinal analysis of time series. Physica A 356, 114–120 (2005)

    Article  Google Scholar 

  14. Szu, H., et al.: Smartphone household wireless electroencephalogram hat. Applied Computational Intelligence and Soft Computing, ID 241489 (2013)

    Google Scholar 

  15. Mammone, N., La Foresta, F., Morabito, F.C.: Automatic artifact rejection from multichannel scalp by EEG by wavelet ICA. IEEE Sensors Journal 12(3), 533–542 (2012)

    Article  Google Scholar 

  16. Wang, Z., Bovik, A.: Mean squared error: Love it or leave it? a new look at signal fidelity measures. IEEE Signal Processing Magazine 26(1), 98–117 (2009)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Domenico Labate .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Labate, D. et al. (2014). EEG Complexity Modifications and Altered Compressibility in Mild Cognitive Impairment and Alzheimer’s Disease. In: Bassis, S., Esposito, A., Morabito, F. (eds) Recent Advances of Neural Network Models and Applications. Smart Innovation, Systems and Technologies, vol 26. Springer, Cham. https://doi.org/10.1007/978-3-319-04129-2_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-04129-2_17

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-04128-5

  • Online ISBN: 978-3-319-04129-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics