Advertisement

Optimization of Recurrence Quantification Analysis for Detecting the Presence of Multiple Sclerosis

  • Simona CarrubbaEmail author
  • Clifton Frilot2nd
  • Andrew A. Marino
Original Article
  • 13 Downloads

Abstract

Purpose

The visual patterns in recurrence plots of time-series data can be quantified using recurrence quantification analysis (RQA), a phase-space-based method. The ability to quantitate recurrence plots affords the possibility of using them to solve central biomedical problems, for example detecting the presence of neurological diseases. Our goal was to assess this application by statistically comparing the values of plot-based quantifiers of electroencephalograms (EEGs) from patients having multiple sclerosis (MS) with values from the EEGs of control subjects.

Methods

First, employing a model system consisting of the addition of known deterministic signals to the EEG of normal subjects, we empirically identified the embedding conditions that facilitated detection of the effect of the addition of the signals. Second, we used the conditions thus identified to compare EEGs from 10 patients with MS and 10 age- and gender-matched control subjects, using seven standard recurrence-plot quantifiers.

Results

We identify embedding dimension of 5 points and time delay of 5 points as conditions that maximize the ability of RQA to detect the presence of deterministic activity in EEGs time series sampled at 500 Hz. The values of the RQA quantifiers computed from the EEGs of the MS patients were significantly greater than the corresponding values from the controls, indicating that the presence of the disease was associated with detectable changes in the EEG (family-wise error < 0.05%).

Conclusions

Recurrence plots detected the occurrence of alterations in EEGs associated with the presence of MS, indicating a decreased complexity (increased order) of brain electrical activity associated with brain disease.

Keywords

Recurrence plot Nonlinear modeling Electroencephalogram Multiple sclerosis 

References

  1. 1.
    Carrubba, S., Frilot, C., Chesson, A. L., & Marino, A. A. (2007). Evidence of a nonlinear human magnetic sense. Neuroscience, 144(1), 356–367.CrossRefGoogle Scholar
  2. 2.
    Frilot, C., II, Kim, P. Y., Carrubba, S., McCarty, D. E., Chesson, A. L., Jr., & Marino, A. A. (2014). Recurrence Quantification Analysis: Understanding Complex Systems. In C. Webber & N. Marwan (Eds.), Analysis of brain recurrence (pp. 213–251). Switzerland: Springer.Google Scholar
  3. 3.
    Schinkel, S., Marwan, N., & Kurths, J. (2009). Brain signal analysis based on recurrences. Journal of Physiology Paris, 103(6), 315–323.CrossRefGoogle Scholar
  4. 4.
    Eckmann, J. P., Kamphorst, S. O., & Ruelle, D. (1987). Recurrence plots of dynamical systems. Europhysics Letters, 4, 973–979.CrossRefGoogle Scholar
  5. 5.
    Zbilut, J. P., & Webber, C. L. (1992). Embedding and delays as derived from quantification of recurrence plot. Physics Letters A, 171, 199–203.CrossRefGoogle Scholar
  6. 6.
    Webber, C. L., & Zbilut, J. P. (1994). Dynamical assessment of physiological systems and states using recurrence plot strategies. Journal of Applied Physiology, 76(2), 965–973.CrossRefGoogle Scholar
  7. 7.
    Webber, C. L., Schmidt, M. A., & Walsh, J. M. (1995). Influence of isometric loading on biceps EMG Dynamics as assessed by linear and nonlinear tools. Journal of Applied Physiolology, 78(3), 814–822.CrossRefGoogle Scholar
  8. 8.
    Apthorp, D., Nagle, F., & Palmisano, S. (2014). Chaos in balance: Non-linear measures of postural control predict individual variations in visual illusions of motion. PLoS ONE, 9(12), e113897.CrossRefGoogle Scholar
  9. 9.
    Liang, Q. Z., Guo, X. M., Zhang, W. Y., et al. (2015). Identification of heart sounds with arrhythmia based on recurrence quantification analysis and kolmogorov entropy. Journal of Medical and Biological Engineering, 35, 209–217.CrossRefGoogle Scholar
  10. 10.
    Timothy, L. T., Krishna, B. M., & Nair, U. (2017). Classification of mild impairment EEG using combined recurrence and cross quantification analysis. International Journal of Psychophysiology, 120, 86–90.CrossRefGoogle Scholar
  11. 11.
    Song, I. H., Lee, D. S., & Kim, S. I. (2004). Recurrence quantification analysis of sleep electroencephalogram in sleep apnea syndrome in humans. Neuroscience Letters, 366(2), 148–153.CrossRefGoogle Scholar
  12. 12.
    Carrubba, S., Kim, P. Y., McCarty, D. E., Chesson, A. L., Jr., Frilot, C., 2nd, & Marino, A. A. (2012). Continuous EEG-based dynamic markers for sleep depth and phasic event. Journal of Neuroscience Methods, 208(1), 1–9.CrossRefGoogle Scholar
  13. 13.
    Martin-Gonzalez, S., Navarro-Mesa, J. L., Julia-Serda, G., Ramirez-Avila, G. M., & Ravelo-Garcia, A. G. (2018). Improving the understanding of sleep apnea characterization using recurrence quantification analysis by defining overall acceptable values for the dimensionality of the system, the delay, and the distance threshold. PLoS ONE, 13(4), e0194462.CrossRefGoogle Scholar
  14. 14.
    Ouyang, G., Li, X., Dang, C., & Richards, D. A. (2008). Using recurrence plot for determinism analysis of EEG recordings in genetic absence epilepsy rats. Clinical Neurophysiology, 119(8), 1747–1755.CrossRefGoogle Scholar
  15. 15.
    Carrubba, S., Frilot, C., Chesson, A. L., Jr., & Marino, A. A. (2006). Detection of nonlinear event-related potentials. Journal of Neuroscience Methods, 157(1), 39–47.CrossRefGoogle Scholar
  16. 16.
    Carrubba, S., Frilot, C., 2nd, & Marino, A. A. (2010). Numerical analysis of recurrence plots to detect effect of environmental-strength magnetic fields on human brain electrical activity. Medical Engineering & Physics, 32(8), 898–907.CrossRefGoogle Scholar
  17. 17.
    Carrubba, S., Minagar, A., Gonzalez-Toledo, E., Chesson, A. L., Jr., Frilot, C., 2nd, & Marino, A. A. (2010). Multiple sclerosis impairs ability to detect abrupt appearance of a subliminal stimulus. Neurological Research, 32(3), 297–302.CrossRefGoogle Scholar
  18. 18.
    Hasan, K. M., Walimuni, I. S., Abid, H., Frye, R. E., Ewing-Cobbs, L., Wolinsky, J. S., et al. (2011). Multimodal quantitative magnetic resonance imaging of thalamic development and aging across the human lifespan: Implications to neurodegeneration in multiple sclerosis. Journal of Neuroscience, 31(46), 16826–16832.CrossRefGoogle Scholar
  19. 19.
    Torabi, A., Daliri, M. R., & Sabzposhan, S. H. (2017). Diagnosis of multiple sclerosis from EEG signals using nonlinear methods. Australasian Physical and Engineering Sciences in Medicine, 40(4), 785–797.CrossRefGoogle Scholar
  20. 20.
    McDonald, W. I., Compston, A., Edan, G., Goodkin, D., Hartung, H. P., Lublin, F. D., et al. (2001). Recommended diagnostic criteria for multiple sclerosis: Guidelines from the international panel on the diagnosis of multiple sclerosis. Annals of Neurology, 50(1), 121–127.CrossRefGoogle Scholar
  21. 21.
    Kurtzke, J. F. (1983). Rating Neurologic impairment in multiple sclerosis: An expanded disability status scale (EDSS). Neurology, 33(11), 1444–1452.CrossRefGoogle Scholar
  22. 22.
    Marwan, N., Wessel, N., Meyerfeldt, U., Schirdewan, A., & Kurths, J. (2002). Recurrence plot based measures of complexity and its application to heart rate variability data. Physical Review E, 66(2), 026702.CrossRefzbMATHGoogle Scholar
  23. 23.
    Abarbanel, H. D. (1996). Analysis of observed chaotic data. New York: Springer.CrossRefzbMATHGoogle Scholar
  24. 24.
    Fraser, A. M., & Swinney, H. L. (1986). Independent coordinates for strange attractors from mutual information. Physical Review A, 33(2), 1134–1140.MathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    Kelly, A. M., Uddin, L. Q., Biswal, B. B., Castellanos, F. X., & Milham, M. P. (2008). Competition between functional brain networks mediates behavioral variability. Neuroimage, 39(1), 527–537.CrossRefGoogle Scholar
  26. 26.
    Lewis, C. M., Baldassarre, A., Committeri, G., Romani, G. L., & Corbetta, M. (2009). Learning sculpts the spontaneous activity of the resting human brain. Proceedings of the National academy of Sciences of the United States of America, 106(41), 17558–17563.CrossRefGoogle Scholar
  27. 27.
    Liu, Y., Gao, J. H., Liotti, M., Pu, Y., & Fox, P. T. (1999). Temporal dissociation of parallel processing in the human subcortical outputs. Nature, 400(6742), 364–367.CrossRefGoogle Scholar
  28. 28.
    Jeong, J. (2004). EEG dynamics in patients with Alzheimer’s disease. Clinical Neurophysiology, 115(7), 1490–1505.CrossRefGoogle Scholar
  29. 29.
    Hornero, R., Abasolo, D., Escudero, J., & Gomez, C. (2009). Nonlinear analysis of electroencephalogram and magnetoencephalogram recordings in patients with alzheimer’s disease. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 367(1887), 317–336.MathSciNetCrossRefzbMATHGoogle Scholar
  30. 30.
    Schmit, J. M., Riley, M. A., Dalvi, A., Sahay, A., Shear, P. K., et al. (2006). Deterministic center of pressure patterns characterize postural instability in parkinson’s disease. Experimental Brain Research, 168(3), 357–367.CrossRefGoogle Scholar
  31. 31.
    Leocani, L., Locatelli, T., Martinelli, V., Rovaris, M., Falautano, M., Filippi, M., et al. (2000). Electroencephalographic coherence analysis in multiple sclerosis: Correlation with clinical, neuropsychological, and MRI findings. Journal of Neurology, Neurosurgery and Psychiatry, 69(2), 192–198.CrossRefGoogle Scholar
  32. 32.
    Leocani, L., Gonzalez-Rosa, J. J., & Comi, G. (2010). Neurophysiological correlates of cognitive disturbances in multiple sclerosis. Neurological Sciences, 31, S249–S253.CrossRefGoogle Scholar
  33. 33.
    Babiloni, C., Del Percio, C., Capotosto, P., Noce, G., Infarinato, F., Muratori, C., et al. (2016). Cortical sources of resting state electroencephalographic rhythms differ in relapsing-remitting and secondary progressive multiple sclerosis. Clinical Neurophysiology, 127(1), 581–590.CrossRefGoogle Scholar
  34. 34.
    Babiloni, C., Frisoni, G. B., Vecchio, F., Pievani, M., Geroldi, C., De Carli, C., et al. (2010). Global functional coupling of resting EEG rhythms is related to white-matter lesions along the cholinergic tracts in subjects with amnesic mild cognitive impairment. Journal of Alzheimer’s Disease, 19(3), 859–871.CrossRefGoogle Scholar
  35. 35.
    Blinowska, K. J., Rakowski, F., Kaminski, M., De Vico, F., Del Percio, C., et al. (2017). Functional and effective brain connectivity for discrimination between Alzheimer’s patients and healthy individuals: A study on resting state EEG rhythms. Clinical Neurophysiology, 128(4), 667–680.CrossRefGoogle Scholar
  36. 36.
    Ranasinghe, K. G., Hinkley, L. B., Beagle, A. J., Mizuiri, D., Honma, S. M., Welch, A. E., et al. (2017). Distinct spatiotemporal patterns of neuronal functional connectivity in primary progressive aphasia variants. Brain, 140(10), 2737–2751.CrossRefGoogle Scholar
  37. 37.
    Iwanski, J. S., & Bradley, E. (1998). Recurrence plots of experimental data: To embed or not to embed? Chaos, 8, 861–871.CrossRefGoogle Scholar

Copyright information

© Taiwanese Society of Biomedical Engineering 2019

Authors and Affiliations

  1. 1.Department of PhysicsMercyhurst UniversityErieUSA
  2. 2.School of Allied Health ProfessionsLouisiana State University Health Sciences CenterShreveportUSA
  3. 3.Department of NeurologyLouisiana State University Health Sciences CenterShreveportUSA

Personalised recommendations