[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Springer Nature Link
Log in

The shape of dementia: new measures of morphological complexity in event-related potentials (ERP) and its application to the detection of Alzheimer’s disease

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

In this paper, we address the problem of quantifying the commonly observed disorganization of the stereotyped wave form of the ERP associated with the P300 component in patients with Alzheimer’s disease. To that extent, we propose two new measures of complexity which relate the spectral content of the signal with its temporal waveform: the spectral matching coefficient and the spectral matching entropy. We show by means of experiments that those measures effectively measure complexity and are related to the shape in an intuitive way. Those indexes are compared with commonly used measures of complexity when comparing AD patients against age-matched healthy controls. The results indicate that AD ERP signals are, indeed, more complex in the shape than that of controls, and this result is evidenced mainly by means of our new measures which have a better performance compared to similar ones. Finally, we try to explain this increase in complexity in light of the communication through coherence hypothesis framework, relating commonly found changes in the EEG with our own results.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Abásolo D, Escudero J, Hornero R, Gómez C, Espino P (2008) Approximate entropy and auto mutual information analysis of the electroencephalogram in Alzheimer’s disease patients. Med Biol Eng Comput 46(10):1019–1028

    Article  PubMed  Google Scholar 

  2. Aviyente S, Brakel LA, Kushwaha RK, Snodgrass M, Shevrin H, Williams WJ (2004) Characterization of event related potentials using information theoretic distance measures. IEEE Trans Biomed Eng 51(5):737–743

    Article  PubMed  Google Scholar 

  3. Bandt C, Pompe B (2002) Permutation entropy: a natural complexity measure for time series. Phys Rev Lett 88(17):174102

    Article  PubMed  Google Scholar 

  4. Bar-Hillel M, Wagenaar WA (1991) The perception of randomness. Adv Appl Math 12(4):428–454

    Article  Google Scholar 

  5. Berger H (1931) Über das Elektrenkephalogramm des Menschen. Eur Arch Psychiat Clin Neurosci 94(1):16–60

    Google Scholar 

  6. Cover TM, Thomas JA (2012) Elements of information theory. Wiley, Hoboken

    Google Scholar 

  7. 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

    Article  CAS  PubMed  Google Scholar 

  8. Dauwels J, Vialatte F, Musha T, Cichocki A (2010) A comparative study of synchrony measures for the early diagnosis of Alzheimer’s disease based on EEG. NeuroImage 49(1):668–693

    Article  CAS  PubMed  Google Scholar 

  9. Dryden IL, Mardia KV (1998) Statistical shape analysis, vol 4. Wiley, New York

    Google Scholar 

  10. Fries P (2005) A mechanism for cognitive dynamics: neuronal communication through neuronal coherence. Trends Cognit Sci 9(10):474–480

    Article  Google Scholar 

  11. Howe AS, Bani-Fatemi A, De Luca V (2014) The clinical utility of the auditory P300 latency subcomponent event-related potential in preclinical diagnosis of patients with mild cognitive impairment and Alzheimer’s disease. Brain Cognit 86:64–74

    Article  Google Scholar 

  12. Jeong J (2004) EEG dynamics in patients with Alzheimer’s disease. Clin Neurophysiol 115(7):1490–1505

    Article  PubMed  Google Scholar 

  13. Luck SJ (2014) An introduction to the event-related potential technique. MIT press, Cambridge

    Google Scholar 

  14. Minati L, Edginton T, Bruzzone MG, Giaccone G (2009) Reviews: current concepts in alzheimer’s disease: a multidisciplinary review. Am J Alzheimer’s Dis Other Dement 24(2):95–121

    Article  Google Scholar 

  15. Page DL, Koschan A, Sukumar SR, Roui-Abidi B, Abidi MA (2003) Shape analysis algorithm based on information theory. In: Image processing, 2003. ICIP 2003. Proceedings. 2003 international conference on (IEEE), vol 1, pp I–229

  16. Papaliagkas V, Kimiskidis V, Tsolaki M, Anogianakis G (2008) Usefulness of event-related potentials in the assessment of mild cognitive impairment. BMC Neurosci 9(1):107

    Article  PubMed Central  PubMed  Google Scholar 

  17. Pfefferbaum A, Ford JM, Kraemer HC (1990) Clinical utility of long latency ’cognitive’ event-related potentials (P3): the cons. Electroencephalogr Clin Neurophysiol 76(1):6–12

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  19. Polich J, Ladish C, Bloom FE (1990) P300 assessment of early Alzheimer’s disease. Electroencephalogr Clin Neurophysiol Evoked Potentials Sect 77(3):179–189

    Article  CAS  Google Scholar 

  20. Polich J (2007) Updating P300: an integrative theory of P3a and P3b. Clin Neurophysiol 118(10):2128–2148

    Article  PubMed Central  PubMed  Google Scholar 

  21. Rankine L, Mesbah M, Boashash B (2007) A matching pursuit-based signal complexity measure for the analysis of newborn EEG. Med Biol Eng Comput 45(3):251–260

    Article  CAS  PubMed  Google Scholar 

  22. Rezek I, Roberts SJ (1998) Stochastic complexity measures for physiological signal analysis. IEEE Trans Biomed Eng 45(9):1186–1191

    Article  CAS  PubMed  Google Scholar 

  23. Richman JS, Moorman JR (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 

  24. Rosso OA, Blanco S, Yordanova J, Kolev V, Figliola A, Schürmann M, Başar E (2001) Wavelet entropy: a new tool for analysis of short duration brain electrical signals. J Neurosci Methods 105(1):65–75

    Article  CAS  PubMed  Google Scholar 

  25. Sethna JP (2006) Statistical mechanics: entropy, order parameters, and complexity, vol 14. Oxford University Press, Oxford

    Google Scholar 

  26. Shalizi CR, Crutchfield JP (2001) Computational mechanics: pattern and prediction, structure and simplicity. J Stat Phys 104(3–4):817–879

    Article  Google Scholar 

  27. Tsai PH, Lin C, Tsao J, Lin PF, Wang PC, Huang NE, Lo MT (2012) Empirical mode decomposition based detrended sample entropy in electroencephalography for Alzheimer’s disease. J Neurosci Methods 210(2):230–237

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

We want to thank Laura R. Giraldo and Kelly J. Cardona for their useful help in the early stages of the project. Special thanks to the anonymous reviewers for their helpful feedback. This work was founded by the internship Jóvenes Investigadores of the Administrative Department of Science, Technology and Innovation of Colombia (Colciencias) and the Universidad Autónoma de Manizales, Colombia, and also supported by the Análisis de la dinámica de características temporales y espectrales de los Potenciales Relacionados a Eventos Cognitivos en pacientes con enfermedad de Alzheimer y sujetos sanos, research Project cod. 310-035.

Author information

Authors and Affiliations

  1. Laboratorio de Neurofisiología, Neuroaprendizaje Research Group, Universidad Autónoma de Manizales, Manizales, Colombia

    A. Jimenez-Rodríguez, J. L. Rodríguez-Sotelo, A. Osorio-Forero, J. M. Medina & F. Restrepo de Mejía

  2. Clínica de la Memoria de Manizales, Manizales, Colombia

    J. M. Medina

Authors
  1. A. Jimenez-Rodríguez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. J. L. Rodríguez-Sotelo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. Osorio-Forero
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. J. M. Medina
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. F. Restrepo de Mejía
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to F. Restrepo de Mejía.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jimenez-Rodríguez, A., Rodríguez-Sotelo, J.L., Osorio-Forero, A. et al. The shape of dementia: new measures of morphological complexity in event-related potentials (ERP) and its application to the detection of Alzheimer’s disease. Med Biol Eng Comput 53, 889–897 (2015). https://doi.org/10.1007/s11517-015-1283-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-015-1283-x

Keywords

Search

Navigation