Skip to main content

Exploration of EEG features of Alzheimer’s disease using continuous wavelet transform

Medical & Biological Engineering & Computing volume 53, pages 843–855 (2015)Cite this article

Abstract

We have developed a novel approach to elucidate several discriminating EEG features of Alzheimer’s disease. The approach is based on the use of a variety of continuous wavelet transforms, pairwise statistical tests with multiple comparison correction, and several decision tree algorithms, in order to choose the most prominent EEG features from a single sensor. A pilot study was conducted to record EEG signals from Alzheimer’s disease (AD) patients and healthy age-matched control (CTL) subjects using a single dry electrode device during several eyes-closed (EC) and eyes-open (EO) resting conditions. We computed the power spectrum distribution properties and wavelet and sample entropy of the wavelet coefficients time series at scale ranges approximately corresponding to the major brain frequency bands. A predictive index was developed using the results from statistical tests and decision tree algorithms to identify the most reliable significant features of the AD patients when compared to healthy controls. The three most dominant features were identified as larger absolute mean power and larger standard deviation of the wavelet scales corresponding to 4–8 Hz (\(\theta\)) during EO and lower wavelet entropy of the wavelet scales corresponding to 8–12 Hz (\(\alpha\)) during EC, respectively. The fourth reliable set of distinguishing features of AD patients was lower relative power of the wavelet scales corresponding to 12–30 Hz (\(\beta\)) followed by lower skewness of the wavelet scales corresponding to 2–4 Hz (upper \(\delta\)), both during EO. In general, the results indicate slowing and lower complexity of EEG signal in AD patients using a very easy-to-use and convenient single dry electrode device.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

43,34 €

Price includes VAT (Finland)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

References

  1. Abasolo D, Hornero R, Espino P, Alvarez D, Poza J (2006) Entropy analysis of the EEG background activity in Alzheimer’s disease patients. Physiol Meas 27(3):241–253

    CAS  Article  PubMed  Google Scholar 

  2. Adeli H, Zhou Z, Dadmehr N (2003) Analysis of EEG records in an epileptic patient using wavelet transform. J Neurosci Methods 123(1):69–87

    Article  PubMed  Google Scholar 

  3. Akin M (2002) Comparison of wavelet transform and FFT methods in the analysis of EEG signals. J Med Syst 26(3):241–247

    CAS  Article  PubMed  Google Scholar 

  4. Avanzo C, Tarantino V, Bisiacchi P, Sparacino G (2009) A wavelet methodology for EEG time-frequency analysis in a time discrimination task. Int J Bioelectromagn 11(4):185–188

    Google Scholar 

  5. Bassani T, Nievola JC (2008) Pattern recognition for brain-computer interface on disabled subjects using a wavelet transformation. In: IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology. Sun Valley, USA, pp 180–186

  6. Bennys K, Rondouin G, Vergnes C, Touchon J (2001) Diagnostic value of quantitative EEG in Alzheimer’s disease. Clin Neurophysiol 31(3):153–160

    CAS  Article  Google Scholar 

  7. Bostanov V (2004) BCI competition 2003-data sets Ib and IIb: feature extraction from event-related brain potentials with the continuous wavelet transform and the t-value scalogram. IEEE Trans Biomed Eng 51(6):1057–1061

    Article  PubMed  Google Scholar 

  8. Chui CK (1992) An introduction to wavelets. Academic Press Professional Inc, San Diego

    Google Scholar 

  9. Darvishi S, Al-Ani A (2007) Brain-computer interface analysis using continuous wavelet transform and adaptive neuro-fuzzy classifier. In: International Conference of the IEEE Engineering in Medicine and Biology Society. Lyon, France, pp 3220–3223

  10. Daubechies I (1992) Ten lectures on wavelets. Society for Industrial and Applied Mathematics, Philadelphia

    Book  Google Scholar 

  11. Dauwels J, Vialatte F, Cichocki A (2010) Diagnosis of Alzheimer’s disease from EEG signals: where are we standing. Curr Alzheimer Res 7(6):487–505

    CAS  Article  PubMed  Google Scholar 

  12. Delorme A (2009) EEG / ERP data available for free public download. http://sccn.ucsd.edu/~arno/fam2data/publicly_available_EEG_data.html

  13. Elgendi M, Vialatte F, Cichocki A, Latchoumane C, Jeong J, Dauwels J (2011) Optimization of EEG frequency bands for improved diagnosis of Alzheimer disease. In: International Conference of the IEEE Engineering in Medicine and Biology Society. Boston, MA, pp 6087–6091

  14. Ghorbanian P, Devilbiss D, Verma A, Bernstein A, Hess T, Simon A, Ashrafiuon H (2013) Identification of resting and active state EEG features of Alzheimer's disease using discrete wavelet transform. Ann Biomed Eng 41(6):1243–1257

    Article  PubMed  Google Scholar 

  15. Ghorbanian P, Devilbiss D, Simon A, Ashrafiuon H (2012) Power based analysis of single-electrode human EEG recordings using continuous wavelet transform. In: 38th Annual Northeast Bioengineering Conference. Philadelphia, PA, pp 279–280

  16. Huang N, Shen S (eds) (2005) Hilbert–Huang transform and its applications, vol 5., Interdisciplinary mathematical sciences World Scientific, Singapore

    Google Scholar 

  17. Jeong J (2004) EEG dynamics in patients with Alzheimer’s disease. Clin Neurophysiol 15(7):1490–1505

    Article  Google Scholar 

  18. Lake D, Moorman J (2011) Accurate estimation of entropy in very short physiological time series: the problem of atrial fibrillation detection in implanted ventricular devices. Am J Physiol Heart Circ Physiol 300(1):H319–H325

    CAS  Article  PubMed  Google Scholar 

  19. Leiser S, Dunlop J, Bowlby M, Devilbiss D (2011) Aligning strategies for using EEG as a surrogate biomarker: a review of preclinical and clinical research. Biochem Pharmacol 81(12):1408–1421

    CAS  Article  PubMed  Google Scholar 

  20. McBride J, Zhao X, Munro N, Smith C, Jicha G, Hively L, Broster L, Schmitt F, Kryscio R, Jiang Y Spectral and complexity analysis of scalp EEG characteristics for mild cognitive impairment and early Alzheimer’s disease. Comput Methods Programs Biomed 114(2):153–163

  21. Mizuno T, Takahashi T, Cho R, Kikuchi M, Murata T, Takahashi K, Wada Y (2010) Assessment of EEG dynamical complexity in Alzheimer's disease using multiscale entropy. Clin Neurophysiol 121(9):1438–1446

    PubMed Central  Article  PubMed  Google Scholar 

  22. Muthuswamy J, Thakor NV (1998) Spectral analysis methods for neurological signals. J Neurosci Methods 83(1):1–14

    CAS  Article  PubMed  Google Scholar 

  23. Pincus S (1991) Approximate entropy as a measure of system complexity. Proc Natl Acad Sci USA 88(6):2297–2301

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  24. Podgorelec V, Kokol P, Stiglic B, Rozman I (2002) Decision trees: an overview and their use in medicine. J Med Syst 26(5):445–463

    Article  PubMed  Google Scholar 

  25. Richman J, Moorman J (2000) Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol 278(6):H2039–H2049

    CAS  PubMed  Google Scholar 

  26. Samar V, Bopardikar A, Rao R, Swartz K (1999) Wavelet analysis of neuroelectric waveforms: a conceptual tutorial. Brain Lang 66(1):7–60

    CAS  Article  PubMed  Google Scholar 

  27. Shannon C (1948) A mathematical theory of communication. Bell Syst Tech J 27(379–423):623–656

    Article  Google Scholar 

  28. Stam CJ (2005) Nonlinear dynamical analysis of EEG and MEG: review of an emerging field. Clin Neurophysiol 116(10):2266–2301

    CAS  Article  PubMed  Google Scholar 

  29. Staudinger T, Polikar R (2011) Analysis of complexity based EEG features for the diagnosis of Alzheimer’s disease. In: International Conference of the IEEE Engineering in Medicine and Biology Society. Boston, MA, pp 2033–2036

  30. Storey JD (2002) A direct approach to false discovery rates. J R Stat Soc 64(3):479–498

    Article  Google Scholar 

  31. Ueda T, Musha T, Yagi T (2009) Research of the characteristics of Alzheimer’s disease using EEG. In: International Conference of the IEEE Engineering in Medicine and Biology Society. Minnesota, USA, pp 4998–5001

  32. Xu G, Zhang X, Yu H, Ho S, Yang Q, Fu W, Yan W (2010) Complexity analysis of EEG under magnetic stimulation at acupoints. IEEE Trans Appl Supercond 20(3):1029–1032

    Article  Google Scholar 

  33. Zambon M, Lawrence R, Bunn A, Powell S (2006) Effect of alternative splitting rules on image processing using classification tree analysis. Photogramm Eng Remote Sensing 72(1):25–30

    Article  Google Scholar 

Download references

Author information

Affiliations

  1. Center for Nonlinear Dynamics and Control, Villanova University, Villanova, PA, 19085, USA

    Parham Ghorbanian & Hashem Ashrafiuon

  2. NexStep Biomarkers LLC, Madison, WI, 53705, USA

    David M. Devilbiss

  3. Palm Drive Hospital, Sebastopol, CA, 95472, USA

    Terry Hess & Allan Bernstein

  4. Cerora Inc, Doylestown, PA, 18902, USA

    Adam J. Simon

Authors
  1. Parham Ghorbanian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. David M. Devilbiss
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Terry Hess
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Allan Bernstein
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Adam J. Simon
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hashem Ashrafiuon
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hashem Ashrafiuon.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ghorbanian, P., Devilbiss, D.M., Hess, T. et al. Exploration of EEG features of Alzheimer’s disease using continuous wavelet transform. Med Biol Eng Comput 53, 843–855 (2015). https://doi.org/10.1007/s11517-015-1298-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-015-1298-3

Keywords

  • EEG
  • Continuous wavelet transform
  • Entropy
  • Decision tree
  • Alzheimer’s disease