Skip to main content

Advertisement

Log in

Classification among healthy, mild cognitive impairment and Alzheimer’s disease subjects based on wavelet entropy and relative beta and theta power

  • Original Article
  • Published:
Pattern Analysis and Applications Aims and scope Submit manuscript

Abstract

Diagnosis of Alzheimer’s disease (AD), mild cognitive impairment (MCI), and healthy subjects (Healthy) is currently lacking an automated tool. It requires experience of neuropsychologists and has sensibilities of 80% when separating between Healthy and MCI. The aim of this work is to evaluate the performance of a method for classification among the three groups using a database of 17 Healthy, 9 MCI and 15 AD. The method uses wavelet decomposition of the EEG signal (Haar mother wavelet and 5 decomposition levels) to calculate the wavelet entropy and theta and beta relative power of the EEG signal. These features are used as inputs to a three-way classifier consisting in a support vector machine with polynomial kernel and a two-layer neural network. The last implements a vote procedure. Wavelet entropy was evaluated together with the sample entropy and approximated entropy to choose the one that best detected changes in the complexity of the EEG signal. The results show that it is possible to automatically classify a subject of a particular group with an overall accuracy of 92.6%, close to the best result found in the literature that is 97.9%. The method could be the basis for the implementation of a diagnosis-support quantitative tool oriented to aid in clinical diagnosis, especially when the classification between the three groups is not one of the more represented researches in the consulted literature.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Alzheimer’s A (2017) 2017 Alzheimer’s disease facts and figures. Alzheimer’s Dement 13(4):325–373

    Article  Google Scholar 

  2. A. s. Society (2019) What is Alzheimer’s disease. https://www.alzheimers.org.uk/about-dementia/types-dementia/alzheimers-disease

  3. Ocaña Montoya CM, Montoya Pedrón A, Bolaño Díaz GA (2019) Perfil clínico neuropsicológico del deterioro cognitivo subtipo posible Alzheimer. MEDISAN 23(5):875–891

    Google Scholar 

  4. Alzheimer’s A (2019) 2019 Alzheimer’s disease facts and figures. Alzheimer’s Dement 15(3):321–387

    Article  Google Scholar 

  5. Wang R, Wang J, Li S, Yu H, Deng B, Wei X (2015) Multiple feature extraction and classification of electroencephalograph signal for Alzheimers’ with spectrum and bispectrum. Chaos Interdiscip J Nonlinear Sci 25(1):013110

    Article  MathSciNet  Google Scholar 

  6. Mittal SH (2016) Abnormal levels of consciousness and their electroencephalogram correlation: a review. EC Neurol Rev Article 4(1):30–35

    Google Scholar 

  7. Ya M, Xun W, Wei L, Ting H, Hong Y, Yuan Z (2015) Is the electroencephalogram power spectrum valuable for diagnosis of the elderly with cognitive impairment? Int J Gerontol 9(4):196–200

    Article  Google Scholar 

  8. Cassani R, Estarellas M, San-Martin R, Fraga FJ, Falk TH (2018) Systematic review on resting-state EEG for Alzheimer’s disease diagnosis and progression assessment. Dis Mark 2018:26

    Google Scholar 

  9. Houmani N et al (2018) Diagnosis of Alzheimer’s disease with electroencephalography in a differential framework. PLoS ONE 13(3):e0193607

    Article  Google Scholar 

  10. Musaeus CS et al (2018) EEG theta power is an early marker of cognitive decline in dementia due to Alzheimer’s disease. J Alzheimers Dis 64(4):1359–1371

    Article  Google Scholar 

  11. Niedermeyer E, da Silva FL (2005) Electroencephalography: basic principles, clinical applications, and related fields. Lippincott Williams & Wilkins, Philadelphia

    Google Scholar 

  12. Al-Jumeily D, Iram S, Vialatte F-B, Fergus P, Hussain A (2015) A novel method of early diagnosis of Alzheimer’s disease based on EEG signals. Sci World J, vol 2015, no Special Issue

  13. Dauwels J (2011) Slowing and loss of complexity in Alzheimer’s EEG: two sides of the same coin? Int J Alzheimer’s Dis 2011:539621

    Google Scholar 

  14. McBride JC et al (2015) Sugihara causality analysis of scalp EEG for detection of early Alzheimer’s disease. NeuroImage Clin 7:258–265

    Article  Google Scholar 

  15. Dauwels J, Vialatte F-B, Cichocki A (2011) On the early diagnosis of Alzheimer’s disease from EEG signals: a mini-review. In: Wang R (ed) Advances in cognitive neurodynamics (II). Springer, Berlin, pp 709–716

    Chapter  Google Scholar 

  16. Abásolo 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

    Article  Google Scholar 

  17. Hornero R, Abásolo D, Escudero J, Gómez C (2009) Nonlinear analysis of electroencephalogram and magnetoencephalogram recordings in patients with Alzheimer’s disease. Philos Trans R Soc Math Phys Eng Sci 367(1887):317–336

    MathSciNet  MATH  Google Scholar 

  18. Coronel C et al (2017) Quantitative EEG markers of entropy and auto mutual information in relation to MMSE scores of probable Alzheimer’s disease patients. Entropy 19(3):130

    Article  Google Scholar 

  19. Al-nuaimi AH, Jammeh E, Sun L, Ifeachor E (2015) Tsallis entropy as a biomarker for detection of Alzheimer’s disease. Presented at the 2015 37th annual international conference of the IEEE engineering in medicine and biology society (EMBC), 2015. https://ieeexplore.ieee.org/abstract/document/7319312

  20. Morabito FC, Labate D, La Foresta F, Bramanti A, Morabito G, Palamara I (2012) Multivariate multi-scale permutation entropy for complexity analysis of Alzheimer’s disease EEG. Entropy 14(7):1186–1202

    Article  Google Scholar 

  21. Zhang D (2019) Wavelet transform. In: Zhang D (ed) Fundamentals of image data mining. Texts in computer science. Springer, Berlin, pp 35–44

    Chapter  Google Scholar 

  22. Rosso OA et al (2001) Wavelet entropy: a new tool for analysis of short duration brain electrical signals. J Neurosci Methods 105(1):65–75

    Article  Google Scholar 

  23. Al-Qazzaz NK, Hamid Bin Mohd Ali S, Ahmad SA, Islam MS, Escudero J (2015) Selection of mother wavelet functions for multi-channel EEG signal analysis during a working memory task. Sensors 15(11):29015–29035

    Article  Google Scholar 

  24. Tatum W IV, Hausain A, Banbadis S, Kaplan P (2008) Handbook of EEG interpretation. Demos Medical Publishing, LLC, New York City

    Google Scholar 

  25. Abásolo D, Hornero R, Espino P, Poza J, Sánchez CI, de la Rosa R (2005) Analysis of regularity in the EEG background activity of Alzheimer’s disease patients with approximate entropy. Clin Neurophysiol 116(8):1826–1834

    Article  Google Scholar 

  26. Sleigh JW, Olofsen E, Dahan A, De Goede J, Steyn-Ross DA (2001) Entropies of the EEG: the effects of general anaesthesia. In: Paper presented at the 5th international conference on memory, awareness and consciousness, 1-3 June 2001, New York

  27. Zanin M, Zunino L, Rosso OA, Papo D (2012) Permutation entropy and its main biomedical and econophysics applications: a review. Entropy 14(8):1553–1577

    Article  Google Scholar 

  28. James G (1998) Majority vote classifiers: theory and applications. In: Dissertation in partial fulfillment of the requirements for the degree of Doctor of Philosophy. Stanford University, Stanford, USA. Available in: http://faculty.marshall.usc.edu/gareth-james/Research/thesis.pdf

  29. Ghorbanian P, Devilbiss DM, Hess T, Bernstein A, Simon AJ, Ashrafiuon H (2015) Exploration of EEG features of Alzheimer’s disease using continuous wavelet transform. Med Biol Eng Comput 53(9):843–855

    Article  Google Scholar 

  30. Uyulan C, ERguzel TT (2016) Comparison of wavelet families for mental task classification. J Neurobehav Sci. https://doi.org/10.5455/JNBS.1454666348

    Article  Google Scholar 

  31. Alomari MH, Awada EA, Samaha A, Alkamha K (2014) Wavelet-based feature extraction for the analysis of EEG signals associated with imagined fists and feet movements. Comput Inf Sci 7(2):17

    Google Scholar 

  32. Jeong D-H, Kim Y-D, Song I-U, Chung Y-A, Jeong J (2016) Wavelet energy and wavelet coherence as EEG biomarkers for the diagnosis of Parkinson’s disease-related dementia and Alzheimer’s disease. Entropy 18(1):8

    Article  Google Scholar 

  33. Castillo AJS (2012) Castillo, Apuntes de Estadística para Ingenieros. Creative Commons, Jaén

  34. Fernández A, Gregorio PG, Maestú F (2012) Actividad espontánea electroencefalográfica y magnetoencefalográfica como marcador de la enfermedad de Alzheimer y el deterioro cognitivo leve. Revista Española de Geriatría y Gerontología 47(1):27–32

    Article  Google Scholar 

  35. Simons S, Espino P, Abásolo D (2018) Fuzzy entropy analysis of the electroencephalogram in patients with Alzheimer’s disease: is the method superior to sample entropy? Entropy 20(1):21

    Article  Google Scholar 

  36. Allan CL, Behrman S, Ebmeier KP, Valkanova V (2017) Diagnosing early cognitive decline: when, how and for whom? Maturitas 96:103–108

    Article  Google Scholar 

  37. Charernboon T (2017) Diagnostic accuracy of the overlapping infinity loops, wire cube, and clock drawing tests for cognitive impairment in mild cognitive impairment and dementia. Int J Alzheimer’s Dis 2017:5289239. https://doi.org/10.1155/2017/5289239

    Article  Google Scholar 

Download references

Acknowledgements

Funding was provided by VLIR.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jorge Esteban Santos Toural.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Santos Toural, J.E., Montoya Pedrón, A. & Marañón Reyes, E.J. Classification among healthy, mild cognitive impairment and Alzheimer’s disease subjects based on wavelet entropy and relative beta and theta power. Pattern Anal Applic 24, 413–422 (2021). https://doi.org/10.1007/s10044-020-00910-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10044-020-00910-8

Keywords

Navigation