Skip to main content
SpringerLink
Log in

Analysis of complexity in the EEG activity of Parkinson’s disease patients by means of approximate entropy

  • Original Article
  • Published:
GeroScience Aims and scope Submit manuscript

Abstract

The objective of the present study is to explore the brain resting state differences between Parkinson’s disease (PD) patients and age- and gender-matched healthy controls (elderly) in terms of complexity of electroencephalographic (EEG) signals. One non-linear approach to determine the complexity of EEG is the entropy. In this pilot study, 28 resting state EEGs were analyzed from 13 PD patients and 15 elderly subjects, applying approximate entropy (ApEn) analysis to EEGs in ten regions of interest (ROIs), five for each brain hemisphere (frontal, central, parietal, occipital, temporal). Results showed that PD patients presented statistically higher ApEn values than elderly confirming the hypothesis that PD is characterized by a remarkable modification of brain complexity and globally modifies the underlying organization of the brain. The higher-than-normal entropy of PD patients may describe a condition of low order and consequently low information flow due to an alteration of cortical functioning and processing of information. Understanding the dynamics of brain applying ApEn could be a useful tool to help in diagnosis, follow the progression of Parkinson’s disease, and set up personalized rehabilitation programs.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Data availability

The data that support the findings of this study are available on request from the corresponding author.

References

  1. Valls-Solé J, Valldeoriola F. Neurophysiological correlate of clinical signs in Parkinson’s disease. Clin Neurophysiol. 2002;113:792–805.

    Article  PubMed  Google Scholar 

  2. Zhang ZX, Roman GC, Hong Z, Wu CB, Qu QM, Huang JB, Zhou B, Geng ZP, Wu JX, Wen HB, Zhao H, Zahner GE. Parkinson’s disease in China: prevalence in Beijing, Xian, and Shanghai. Lancet. 2005;365:595–7.

    Article  PubMed  Google Scholar 

  3. Beitz JM. Parkinson’s disease: a review. Front Biosci (Schol Ed). 2014;6:65–74.

    Article  Google Scholar 

  4. Kalia LV, Lang AE. Parkinson’s disease. Lancet. 2015;386:896–912.

    Article  CAS  PubMed  Google Scholar 

  5. Jankovic J. Parkinson’s disease: clinical features and diagnosis. J Neurol Neurosurg Psychiatry. 2008;79:368–76.

    Article  CAS  PubMed  Google Scholar 

  6. Moore DJ, West AB, Dawson VL, Dawson TM. Molecular pathophysiology of Parkinson’s disease. Annu Rev Neurosci. 2005;28:57–87.

    Article  CAS  PubMed  Google Scholar 

  7. Lindgren HS, Dunnett SB. Cognitive dysfunction and depression in Parkinson’s disease: what can be learned from rodent models? Eur J Neurosci. 2012;35:1894–907.

    Article  PubMed  Google Scholar 

  8. Rossini PM, Filippi MM, Vernieri F. Neurophysiology of sensorimotor integration in Parkinson’s disease. Clin Neurosci. 1998;5:121–30.

    CAS  PubMed  Google Scholar 

  9. Ulivelli M, Rossi S, Pasqualetti P, Rossini PM, Ghiglieri O, Passero S, Battistini N. Time course of frontal somatosensory evoked potentials. Relation to L-dopa plasma levels and motor performance in PD. Neurology. 1999;53:1451–7.

    Article  CAS  PubMed  Google Scholar 

  10. Melgari JM, Curcio G, Mastrolilli F, Salomone G, Trotta L, Tombini M, di Biase L, Scrascia F, Fini R, Fabrizio E, Rossini PM, Vernieri F. Alpha and beta EEG power reflects L-dopa acute administration in parkinsonian patients. Front Aging Neurosci. 2014;6:302.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Miraglia F, Tomino C, Vecchio F, Alù F, Orticoni A, Judica E, Cotelli M, Rossini PM. Assessing the dependence of the number of EEG channels in the brain networks’ modulations. Brain Res Bull. 2020.

  12. Gandal MJ, Edgar JC, Klook K, Siegel SJ. Gamma synchrony: towards a translational biomarker for the treatment-resistant symptoms of schizophrenia. Neuropharmacol. 2012;62:1504–18.

    Article  CAS  Google Scholar 

  13. Hampel H, Frank R, Broich K, Teipel SJ, Katz RG, Hardy J, Herholz K, Bokde AL, Jessen F, Hoessler YC, Sanhai WR, Zetterberg H, Woodcock J, Blennow K. Biomarkers for Alzheimer’s disease: academic, industry and regulatory perspectives. Nat Rev Drug Discov. 2010;9:560–74.

    Article  CAS  PubMed  Google Scholar 

  14. Kheiri F, Bragin A, Engel J, Almajano J, Winden E. Non-linear classification of heart rate parameters as a biomarker for epileptogenesis. Epilepsy Res. 2012;100:59–66.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Leuchter AF, Cook IA, Hunter A, Korb A. Use of clinical neurophysiology for the selection of medication in the treatment of major depressive disorder: the state of the evidence. Clin EEG Neurosci. 2009;40:78–83.

    Article  PubMed  Google Scholar 

  16. Vecchio F, Miraglia F, Alù F, Menna M, Judica E, Cotelli M, Rossini PM. Classification of Alzheimer’s disease with respect to physiological aging with innovative EEG biomarkers in a machine learning implementation. J Alzheimers Dis. 2020.

  17. Stoffers D, Bosboom JL, Deijen JB, Wolters EC, Berendse HW, Stam CJ. Slowing of oscillatory brain activity is a stable characteristic of Parkinson’s disease without dementia. Brain. 2007;130:1847–60.

    Article  CAS  PubMed  Google Scholar 

  18. Serizawa K, Kamei S, Morita A, Hara M, Mizutani T, Yoshihashi H, Yamaguchi M, Takeshita J, Hirayanagi K. Comparison of quantitative EEGs between Parkinson disease and age-adjusted normal controls. J Clin Neurophysiol. 2008;25:361–6.

    Article  PubMed  Google Scholar 

  19. de Weerd AW, Perquin WV. Dementia in Parkinson’s disease. Neurology. 1994;44:1553.

    Article  PubMed  Google Scholar 

  20. Soikkeli R, Partanen J, Soininen H, Pääkkönen A, Riekkinen P. Slowing of EEG in Parkinson’s disease. Electroencephalogr Clin Neurophysiol. 1991;79:159–65.

    Article  CAS  PubMed  Google Scholar 

  21. Neufeld MY, Blumen S, Aitkin I, Parmet Y, Korczyn AD. EEG frequency analysis in demented and nondemented parkinsonian patients. Dementia. 1994;5:23–8.

    CAS  PubMed  Google Scholar 

  22. Vecchio F, Pappalettera C, Miraglia F, Alù F, Orticoni A, Judica E, Cotelli M, Pistoia F, Rossini PM: Graph theory on brain cortical sources in Parkinson's disease: the analysis of 'small world' organization from EEG. Sensors (Basel) 2021;21.

  23. Pezard L, Jech R, Růzicka E. Investigation of non-linear properties of multichannel EEG in the early stages of Parkinson’s disease. Clin Neurophysiol. 2001;112:38–45.

    Article  CAS  PubMed  Google Scholar 

  24. Alù F, Orticoni A, Judica E, Cotelli M, Rossini PM, Miraglia F, Vecchio F: Entropy modulation of electroencephalographic signals in physiological aging. Mech Ageing Dev. 2021;196:111472.

  25. Alù F, Miraglia F, Orticoni A, Judica E, Cotelli M, Rossini PM, Vecchio F: Approximate entropy of brain network in the study of hemispheric differences. Entropy (Basel). 2020;22.

  26. Lainscsek C, Hernandez ME, Weyhenmeyer J, Sejnowski TJ, Poizner H. Non-linear dynamical analysis of EEG time series distinguishes patients with Parkinson’s disease from healthy individuals. Front Neurol. 2013;4:200.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Zhang XD. Entropy for the complexity of physiological signal dynamics. Adv Exp Med Biol. 2017;1028:39–53.

    Article  PubMed  Google Scholar 

  28. Carhart-Harris RL. The entropic brain - revisited. Neuropharmacol. 2018;142:167–78.

    Article  CAS  Google Scholar 

  29. Keshmiri S: Entropy and the brain: an overview. Entropy (Basel). 2020;22.

  30. Rosso OA. Entropy changes in brain function. Int J Psychophysiol. 2007;64:75–80.

    Article  PubMed  Google Scholar 

  31. Chung CC, Kang JH, Yuan RY, Wu D, Chen CC, Chi NF, Chen PC, Hu CJ. Multiscale entropy analysis of electroencephalography during sleep in patients with Parkinson disease. Clin EEG Neurosci. 2013;44:221–6.

    Article  PubMed  Google Scholar 

  32. Yi GS, Wang J, Deng B, Wei XL. Complexity of resting-state EEG activity in the patients with early-stage Parkinson’s disease. Cogn Neurodyn. 2017;11:147–60.

    Article  PubMed  Google Scholar 

  33. Pincus S. Approximate entropy (ApEn) as a complexity measure. Chaos. 1995;5:110–7.

    Article  PubMed  Google Scholar 

  34. Pincus SM, Viscarello RR. Approximate entropy: a regularity measure for fetal heart rate analysis. Obstet Gynecol. 1992;79:249–55.

    CAS  PubMed  Google Scholar 

  35. Bruhn J, Röpcke H, Rehberg B, Bouillon T, Hoeft A. Electroencephalogram approximate entropy correctly classifies the occurrence of burst suppression pattern as increasing anesthetic drug effect. Anesthesiology. 2000;93:981–5.

    Article  CAS  PubMed  Google Scholar 

  36. Posener JA. Charles DeBattista, Veldhuis JD, Province MA, Williams GH, Schatzberg AF: Process irregularity of cortisol and adrenocorticotropin secretion in men with major depressive disorder. Psychoneuroendocrinology. 2004;29:1129–37.

    Article  CAS  PubMed  Google Scholar 

  37. Vecchio F, Miraglia F, Judica E, Cotelli M, Alù F, Rossini PM: Human brain networks: a graph theoretical analysis of cortical connectivity normative database from EEG data in healthy elderly subjects. Geroscience. 2020.

  38. Miraglia F, Vecchio F, Bramanti P, Rossini PM. Small-worldness characteristics and its gender relation in specific hemispheric networks. Neuroscience. 2015;310:1–11.

    Article  CAS  PubMed  Google Scholar 

  39. Miraglia F, Vecchio F, Rossini PM. Searching for signs of aging and dementia in EEG through network analysis. Behav Brain Res. 2017;317:292–300.

    Article  PubMed  Google Scholar 

  40. Miraglia F, Vecchio F, Marra C, Quaranta D, Alù F, Peroni B, Granata G, Judica E, Cotelli M, Rossini PM. Small world index in default mode network predicts progression from mild cognitive impairment to dementia. Int J Neural Syst. 2020;30:2050004.

    Article  PubMed  Google Scholar 

  41. Vecchio F, Tomino C, Miraglia F, Iodice F, Erra C, Di Iorio R, Judica E, Alù F, Fini M, Rossini PM. Cortical connectivity from EEG data in acute stroke: a study via graph theory as a potential biomarker for functional recovery. Int J Psychophysiol. 2019;146:133–8.

    Article  PubMed  Google Scholar 

  42. Vecchio F, Miraglia F, Quaranta D, Lacidogna G, Marra C, Rossini PM. Learning processes and brain connectivity in a cognitive-motor task in neurodegeneration: evidence from EEG network analysis. J Alzheimers Dis. 2018;66:471–81.

  43. Hoffmann S, Falkenstein M: The correction of eye blink artefacts in the EEG: a comparison of two prominent methods. PLoS One. 2008;3:e3004.

  44. Iriarte J, Urrestarazu E, Valencia M, Alegre M, Malanda A, Viteri C, Artieda J. Independent component analysis as a tool to eliminate artifacts in EEG: a quantitative study. J Clin Neurophysiol. 2003;20:249–57.

    Article  PubMed  Google Scholar 

  45. Jung TP, Makeig S, Humphries C, Lee TW, McKeown MJ, Iragui V, Sejnowski TJ. Removing electroencephalographic artifacts by blind source separation. Psychophysiology. 2000;37:163–78.

    Article  CAS  PubMed  Google Scholar 

  46. Miraglia F, Vecchio F, Rossini PM. Brain electroencephalographic segregation as a biomarker of learning. Neural Netw. 2018;106:168–74.

    Article  PubMed  Google Scholar 

  47. Alù F, Orticoni A, Judica E, Cotelli M, Rossini P, Miraglia F, Vecchio F: Entropy modulation of brain electroencephalographic signals in physiological aging (submitted), 2020.

  48. Montesinos L, Castaldo R, Pecchia L. On the use of approximate entropy and sample entropy with centre of pressure time-series. J Neuroeng Rehabil. 2018;15:116.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Lee GM, Fattinger S, Mouthon AL, Noirhomme Q, Huber R. Electroencephalogram approximate entropy influenced by both age and sleep. Front Neuroinform. 2013;7:33.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Abásolo D, Escudero J, Hornero R, Gómez C, Espino P. Approximate entropy and auto mutual information analysis of the electroencephalogram in Alzheimer’s disease patients. Med Biol Eng Comput. 2008;46:1019–28.

    Article  PubMed  Google Scholar 

  51. Abásolo D, Hornero R, Espino P, Poza J, Sánchez CI, de la Rosa R. Analysis of regularity in the EEG background activity of Alzheimer’s disease patients with Approximate Entropy. Clin Neurophysiol. 2005;116:1826–34.

    Article  PubMed  Google Scholar 

  52. Burioka N, Miyata M, Cornélissen G, Halberg F, Takeshima T, Kaplan DT, Suyama H, Endo M, Maegaki Y, Nomura T, Tomita Y, Nakashima K, Shimizu E. Approximate entropy in the electroencephalogram during wake and sleep. Clin EEG Neurosci. 2005;36:21–4.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Pincus SM. Approximate entropy as a measure of system complexity. Proc Natl Acad Sci U S A. 1991;88:2297–301.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Pincus SM. Assessing serial irregularity and its implications for health. Ann N Y Acad Sci. 2001;954:245–67.

    Article  CAS  PubMed  Google Scholar 

  55. Sun R, Wong WW, Wang J, Tong RK. Changes in electroencephalography complexity using a brain computer interface-motor observation training in chronic stroke patients: a fuzzy approximate entropy analysis. Front Hum Neurosci. 2017;11:444.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Vecchio F, Miraglia F, Pappalettera C, Orticoni A, Alù F, Judica E, Cotelli M, Rossini PM. Entropy as measure of brain networks’ complexity in eyes open and closed conditions. Symmetry. 2021;13:2178.

    Article  Google Scholar 

  57. Stern Y. MPTP-induced parkinsonism. Prog Neurobiol. 1990;34:107–14.

    Article  CAS  PubMed  Google Scholar 

  58. Han CX, Wang J, Yi GS, Che YQ. Investigation of EEG abnormalities in the early stage of Parkinson’s disease. Cogn Neurodyn. 2013;7:351–9.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Müller V, Lutzenberger W, Pulvermüller F, Mohr B, Birbaumer N. Investigation of brain dynamics in Parkinson’s disease by methods derived from nonlinear dynamics. Exp Brain Res. 2001;137:103–10.

    Article  PubMed  Google Scholar 

  60. Lafreniere-Roula M, Darbin O, Hutchison WD, Wichmann T, Lozano AM, Dostrovsky JO. Apomorphine reduces subthalamic neuronal entropy in parkinsonian patients. Exp Neurol. 2010;225:455–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Shannon CE. A mathematical theory of communication. Bell Syst Tech J. 1948;27:379–423.

    Article  Google Scholar 

  62. Darbin O, Adams E, Martino A, Naritoku L, Dees D, Naritoku D. Non-linear dynamics in parkinsonism. Front Neurol. 2013;4:211.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Railo H, Suuronen I, Kaasinen V, Murtojärvi M, Pahikkala T, Airola A: Resting state EEG as a biomarker of Parkinson’s disease: influence of measurement conditions. bioRxiv 2020:2020.2005.2008.084343.

Download references

Funding

This work was partially supported by the Italian Ministry of Health for Institutional Research (Ricerca corrente) and by Toto Holding.

Author information

Authors and Affiliations

  1. Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Via Val Cannuta, 247, 00166, Rome, Italy

    Chiara Pappalettera, Francesca Miraglia, Paolo Maria Rossini & Fabrizio Vecchio

  2. Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy

    Chiara Pappalettera, Francesca Miraglia & Fabrizio Vecchio

  3. Neuropsychology Unit, IRCCS Istituto Centro San Giovanni Di DioFatebenefratelli, Brescia, Italy

    Maria Cotelli

Authors
  1. Chiara Pappalettera
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Francesca Miraglia
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Maria Cotelli
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Paolo Maria Rossini
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Fabrizio Vecchio
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

CP: Conceptualization, Methodology, Writing—Original draft preparation.

FM: Supervision, Writing—Reviewing and Editing.

MC: Writing—Reviewing and Editing.

PMR: Writing—Reviewing and Editing.

FV: Conceptualization, Methodology, Writing—Reviewing and Editing.

Corresponding author

Correspondence to Fabrizio Vecchio.

Ethics declarations

Conflict of interests

The authors declare no competing interests.

Additional information

Publisher's note

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

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pappalettera, C., Miraglia, F., Cotelli, M. et al. Analysis of complexity in the EEG activity of Parkinson’s disease patients by means of approximate entropy. GeroScience 44, 1599–1607 (2022). https://doi.org/10.1007/s11357-022-00552-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11357-022-00552-0

Keywords

Search

Navigation