Advertisement

Annals of Biomedical Engineering

, Volume 47, Issue 1, pp 282–296 | Cite as

Algorithmic Complexity of EEG for Prognosis of Neurodegeneration in Idiopathic Rapid Eye Movement Behavior Disorder (RBD)

  • Giulio RuffiniEmail author
  • David Ibañez
  • Eleni Kroupi
  • Jean-François Gagnon
  • Jacques Montplaisir
  • Ronald B. Postuma
  • Marta Castellano
  • Aureli Soria-Frisch
Article

Abstract

Idiopathic rapid eye movement sleep behavior disorder (RBD) is a serious risk factor for neurodegenerative processes such as Parkinson’s disease (PD). We investigate the use of EEG algorithmic complexity derived metrics for its prognosis. We analyzed resting state EEG data collected from 114 idiopathic RBD patients and 83 healthy controls in a longitudinal study forming a cohort in which several RBD patients developed PD or dementia with Lewy bodies. Multichannel data from ~ 3 min recordings was converted to spectrograms and their algorithmic complexity estimated using Lempel–Ziv–Welch compression. Complexity measures and entropy rate displayed statistically significant differences between groups. Results are compared to those using the ratio of slow to fast frequency power, which they are seen to complement by displaying increased sensitivity even when using a few EEG channels. Poor prognosis in RBD appears to be associated with decreased complexity of EEG spectrograms stemming in part from frequency power imbalances and cross-frequency amplitude algorithmic coupling. Algorithmic complexity metrics provide a robust, powerful and complementary way to quantify the dynamics of EEG signals in RBD with links to emerging theories of brain function stemming from algorithmic information theory.

Keywords

Parkinson’s disease Complexity Algorithmic complexity LZW Lempel–Ziv–Welch compression RBD Time-frequency analysis Dementia with Lewy bodies DLB 

Notes

Acknowledgments

This work has been partly supported by the Michael J. Fox Foundation project “Discovery of EEG biomarkers for Parkinson Disease and Lewy Body Dementia using advanced Machine Learning techniques” (Rapid Response Innovation Awards 2013), the FET Open Luminous project (H2020-FETOPEN-2014- 2015-RIA under agreement No. 686764) as part of the European Union’s Horizon 2020 research and training programme 2014-2018, the Canadian Institutes of Health Research (CIHR) and the W. Garfield Weston Foundation.

Disclosure

Starlab and Neuroelectrics authors have an interest in developing commercial, translational applications from this research. GR is a shareholder of both companies.

Author Contributions

DI and MC pre-processed EEG data to produce artifact free spectrograms. EK and ASF contributed to code development and revised the manuscript. JFG, RP and JM collected the EEG data and revised the manuscript. GR provided the complexity analysis code, carried out the complexity and statistical analysis, and wrote the manuscript.

References

  1. 1.
    Abásolo, D., R. Horner, C. Gómez, M. García, and M. López. Analysis of EEG background activity in Alzheimer’s disease patients with Lempel-Ziv complexity and central tendency measure. Med. Eng. Phys. 28(4):315–322, 2006.CrossRefGoogle Scholar
  2. 2.
    Albert, R., and A.-L. Barabasi. Statistical mechanics of complex networksks. Rev. Mod. Phys., 74:47, 2002.CrossRefGoogle Scholar
  3. 3.
    Andrillon, T., A. T. Poulsen, L. K. Hansen, D. Leger, and S. Kouider. Neural markers of responsiveness to the environment in human sleep. J. Neurosci. 36(24):6583–6596, 2016.CrossRefGoogle Scholar
  4. 4.
    Bertrand, J.-A., A. McIntosh, R. Postuma, N. Kovacevic, V. Latreille, M. Panisset, S. Chouinard, and J. Gagnon. Brain connectivity alterations are associated with the development of dementia in Parkinson’s disease. Brain Connect. 6(3):216–224, 2016.CrossRefGoogle Scholar
  5. 5.
    Casali, A. G., O. Gosseries, M. Rosanova, M. Boly, S. Sarasso, K. R. Casali, S. Casarotto, M.-A. Bruno, S. Laureys, G. Tononi, and M. Massimini. A theoretically based index of consciousness independent of sensory processing and behavior. Sci. Transl. Med.5(198):105, 2013.CrossRefGoogle Scholar
  6. 6.
    Cerra, D., and M. Datcu. Expanding the algorithmic information theory frame for applications to earth observation. Entropy 15:407–415, 2013.CrossRefGoogle Scholar
  7. 7.
    Cover, T. M., and J. A. Thomas. Elements of Information Theory, 2nd ed. New York: Wiley, 2006.Google Scholar
  8. 8.
    Dauwels, J., K. Srinivasan, M. R. Reddy, T. Musha, F.-B. Vialatte, C. Latchoumane, J. Jeong, and A. Cichocki. Slowing and loss of complexity in Alzheimer’s EEG: two sides of the same coin? Int. J. Alzheimer’s Dis. 2011:539621, 2011.Google Scholar
  9. 9.
    Fantini, L., J. Gagnon, D. Petit, S. Rompre, A. Decary, J. Carrier, and J. Montplaisir. Slowing of electroencephalogram in rapid eye movement sleep behavior disorder. Ann. Neurol. 53(6):774–780, 2003.CrossRefGoogle Scholar
  10. 10.
    Fulda, S. Idiopathic REM sleep behavior disorder as a long-term predictor of neurodegenerative disorders. EPMA J. 2(4):451–458, 2011.CrossRefGoogle Scholar
  11. 11.
    Gomez, C., R. Hornero, D. Abasolo, M. Lopez, and A. Fernandez. Decreased Lempel-Ziv complexity in Alzheimer’s disease patients’ magnetoencephalograms. Conf. Proc. IEEE Eng. Med. Biol. Soc. 5:4514–4517, 2005.Google Scholar
  12. 12.
    Gomez, C., K. T. E. O. Dubbelin, C. J. Stam, D. Abasolo, H. W. Berendse, and R. Hornero. Complexity analysis of resting-state MEG activity in early-stage Parkinson’s disease patients. Ann. Biomed. Eng. 39(12):2935–2944, 2011.CrossRefGoogle Scholar
  13. 13.
    Grunwald, P., and P. Vitanyi. Shannon information and kolmogorov complexity. arXiv:cs/0410002, 2004.
  14. 14.
    Högl, B., A. Stefani, and A. Videnovic. Idiopathic REM sleep behaviour disorder and neurodegeneration—an update. Nat. Rev. Neurol. 14:40–55, 2018.CrossRefGoogle Scholar
  15. 15.
    Hudetz, A. G., X. Liu, S. Pillay, M. Boly, and G. Tononi. Propofol anesthesia reduces Lempel-Ziv complexity of spontaneous brain activity in rats. Neurosci. Lett. 628:132–135, 2016.CrossRefGoogle Scholar
  16. 16.
    Iranzo, A., A. Fernández-Arcos, E. Tolosa, M. Serradell, J. L. Molinuevo, F. Valldeoriola, E. Gelpi, I. Vilaseca, R. Sánchez-Valle, A. Lladó, C. Gaig. Neurodegenerative disorder risk in idiopathic REM sleep behavior disorder: study in 174 patients. PLoS ONE 9(2):89741, 2014.CrossRefGoogle Scholar
  17. 17.
    Javier, J. M., A. S.-F. Aureli, D. I. Nez, S. Dunne, C. Grau, G. Ruffini, J. Rodrigues-Brazete, R. Postuma, c. G. Jean-Fran J. Montplaisir, and A. Pascual. Advanced machine learning for classification of EEG traits as Parkinson’s biomarker. Front. Neuroinform. 00071, 2014.Google Scholar
  18. 18.
    Jia, Y., H. Gu, Q. Luo. Sample entropy reveals an age-related reduction in the complexity of dynamic. Brain. Sci. Rep. 7(1):7990, 2017.CrossRefGoogle Scholar
  19. 19.
    Kaspar, F., and H. G. Schuster. Easily calculable measure for the complexity of spatiotemporal patterns. Phys. Rev. A 36(2):842–848, 1987.CrossRefGoogle Scholar
  20. 20.
    Kim, Y., Y. E. Kim, E. O. Park, C. W. Shin, H.-J. Kim, and B. Jeon. REM sleep behavior disorder portends poor prognosis in Parkinson’s disease: a systematic review. J. Clin. Neurosci. 47:6–13, 2017.CrossRefGoogle Scholar
  21. 21.
    Latreille, V., J. Carrier, B. Gaudet-Fex, J. Rodrigues-Brazete, M. Panisset, S. Chouinard, R. B. Postuma, and J.-F. Gagnon. Electroencephalographic prodromal markers of dementia across conscious states in Parkinson’s disease. Brain 138:1189–1199, 2016.CrossRefGoogle Scholar
  22. 22.
    Lempel, A., and J. Ziv. On the complexity of finite sequences. IEEE Trans. Inf. Theory 22(1):75–81, 1976.CrossRefGoogle Scholar
  23. 23.
    Li, M., X. Chen, X. Li, B. Ma, and P. M. B. Vitànyi. The similarity metric. IEEE Trans. Inf. Theory 50(12):113859, 2004.CrossRefGoogle Scholar
  24. 24.
    Postuma, R., J. Gagnon, M. Vendette, M. Fantini, J. Massicotte-Marquez, et al. Quantifying the risk of neurodegenerative disease in idiopathic REM sleep behavior disorder. Neurology 72:1296–1300, 2009.CrossRefGoogle Scholar
  25. 25.
    Ray, C., G. Ruffini, J. Marco-Pallarés, L. Fuentemilla, and C. Grau. Complex networks in brain electrical activity. Eur. Phys. Lett. 79:38004, 2007.CrossRefGoogle Scholar
  26. 26.
    Rodrigues-Brazète, J., J. Gagnon, R. Postuma, J. Bertrand, and M. J. D Petit. Electroencephalogram slowing predicts neurodegeneration in rapid eye movement sleep behavior disorder. Neurobiol. Aging 37:74–81, 2016.CrossRefGoogle Scholar
  27. 27.
    Ruffini, G., D. Ibañez, M. Castellano, S. Dunne, and A. Soria-Frisch. EEG-driven RNN classification for prognosis of neurodegeneration in at-risk patients. In: ICANN 2016. Cham: Springer, 2016.Google Scholar
  28. 28.
    Ruffini, G., D. Ibañez Soria, L. Dubreuil, M. Castellano, J. -F. Gagnon, J. Montplaisir, and A. Soria-Frisch. Deep learning with eeg spectrograms in rapid eye movement behavior disorder. BioRXiv 240267, 2018.Google Scholar
  29. 29.
    Ruffini, G. Lempel-Ziv complexity reference. arXiv:1707.09848 [cs.IT], 2017.
  30. 30.
    Ruffini, G. An algorithmic information theory of consciousness. Neurosci. Conscious. 3(1):012 2017.Google Scholar
  31. 31.
    Schartner, M., A. Seth, Q. Noirhomme, M. Boly, M.-A. Bruno, S. Laureys, et al. Complexity of multi-dimensional spontaneous EEG decreases during propofol induced general anaesthesia. PLoS ONE 10(8):0133532, 2015.CrossRefGoogle Scholar
  32. 32.
    Schartner, M. M., R. L. Carhart-Harris, A. B. Barrett, A. K. Seth, and S. D. Muthukumaraswamy. Increased spontaneous MEG signal diversity for psychoactive doses of ketamine, lsd and psilocybin. Sci.Rep. 7:46421, 2017.CrossRefGoogle Scholar
  33. 33.
    Soria-Frisch, A., J. Marin, D. I. Ibañez, S. Dunne, C. Grau, G. Ruffini, J. Rodrigues-Brazète, R. Postuma, J.-F. Gagnon, J. Montplaisir, and A. Pascual-Leone. Machine learning for a Parkinson’s prognosis and diagnosis system based on EEG. In: Proceedings of the International Pharmaco-EEG Society Meeting PEG 2014. Leipzig: Germany, 2014.Google Scholar
  34. 34.
    Tononi, G., M. Boly, M. Massimini, and C. Koch. Integrated information theory: from consciousness to its physical substrate. Nat. Rev. Neurosci. 17:450–461, 2016.CrossRefGoogle Scholar
  35. 35.
    Welch, T. A technique for high-performance data compression. Computer 17(6):8–18, 1984.CrossRefGoogle Scholar
  36. 36.
    Zhang, X.-S., R. J. Roy, and E. W. Jensen. EEG complexity as a measure of depth of anesthesia for patients. IEEE Trans. Biomed. Eng. 48(12):1424–1433, 2001.CrossRefGoogle Scholar

Copyright information

© Biomedical Engineering Society 2018

Authors and Affiliations

  • Giulio Ruffini
    • 1
    Email author return OK on get
  • David Ibañez
    • 1
  • Eleni Kroupi
    • 1
  • Jean-François Gagnon
    • 2
  • Jacques Montplaisir
    • 2
  • Ronald B. Postuma
    • 2
  • Marta Castellano
    • 1
  • Aureli Soria-Frisch
    • 1
  1. 1.Starlab BarcelonaBarcelonaSpain
  2. 2.Centre for Advanced Research in Sleep MedicineHôpital du Sacré-Cœur de MontréalMontrealCanada

Personalised recommendations