Journal of Healthcare Engineering

Journal of Healthcare Engineering / 2015 / Article

Research Article | Open Access

Volume 6 |Article ID 316746 | https://doi.org/10.1260/2040-2295.6.1.55

Joseph McBride, Xiaopeng Zhao, Nancy Munro, Gregory Jicha, Charles Smith, Yang Jiang, "Discrimination of Mild Cognitive Impairment and Alzheimer's Disease Using Transfer Entropy Measures of Scalp EEG", Journal of Healthcare Engineering, vol. 6, Article ID 316746, 16 pages, 2015. https://doi.org/10.1260/2040-2295.6.1.55

Discrimination of Mild Cognitive Impairment and Alzheimer's Disease Using Transfer Entropy Measures of Scalp EEG

Received01 Apr 2014
Accepted01 Dec 2014

Abstract

Mild cognitive impairment (MCI) is a neurological condition related to early stages of dementia including Alzheimer's disease (AD). This study investigates the potential of measures of transfer entropy in scalp EEG for effectively discriminating between normal aging, MCI, and AD participants. Resting EEG records from 48 age-matched participants (mean age 75.7 years)—15 normal controls, 16 MCI, and 17 early AD—are examined. The mean temporal delays corresponding to peaks in inter-regional transfer entropy are computed and used as features to discriminate between the three groups of participants. Three-way classification schemes based on binary support vector machine models demonstrate overall discrimination accuracies of 91.7— 93.8%, depending on the protocol condition. These results demonstrate the potential for EEG transfer entropy measures as biomarkers in identifying early MCI and AD. Moreover, the analyses based on short data segments (two minutes) render the method practical for a primary care setting.

References

  1. R. C. Petersen, R. Doody, A. Kurz et al., “Current concepts in mild cognitive impairment,” Arch. Neurol, vol. 58, pp. 1985–1992, 2001. View at: Google Scholar
  2. R. C. Petersen, Mild cognitive impairment, Oxford Press, New York, NY, 2003.
  3. T. Schreiber, “Measuring information transfer,” Phys. Rev. Lett, vol. 85, pp. 461–464, 2000. View at: Google Scholar
  4. K. Hlaváčková-Schindler, M. Palus, M. Vejmelka, and J. Bhattacharya, “Causality detection based on information-theoretic approaches in time series analysis,” Phys. Rep, vol. 441, pp. 1–46, 2007. View at: Google Scholar
  5. L. Barnett, “Granger causality and transfer entropy are equivalent for Gaussian variables,” Phys. Rev. Lett, Article ID 238701, p. 103, 2009. View at: Google Scholar
  6. J. Lizier, J. Heinzle, A. Horstmann, J. Haynes, and M. Prokopenko, “Multivariate information-theoretic measures reveal directed information structure and task relevant changes in fMRI connectivity,” J. Comput. Neurosci, vol. 30, pp. 85–107, 2011. View at: Google Scholar
  7. R. Vicente, M. Vibral, M. Lindner, and G. Pipa, “Transfer entropya model-free measure of effective connectivity for the neurosciences,” J. Comput. Neurosci, vol. 30, pp. 45–67, 2011. View at: Google Scholar
  8. J. Dauwels, F. Vialatte, and A. Cichocki, “Diagnosis of Alzheimer's disease from EEG signals: where are we standing?” Curr. Alzheimer Res, vol. 7, no. 6, pp. 487–505, 2010. View at: Google Scholar
  9. C. J. Stam, Y. van der Made, Y. A. Pijnenburg, and P. Scheltens, “EEG synchronization in mild cognitive impairment and Alzheimer's disease,” Acta Neurol. Scand, vol. 108, pp. 90–96, 2003. View at: Google Scholar
  10. C. J. Stam, T. Montex, B. F. Jones et al., “Disturbed fluctuations of resting state EEG synchronization in Alzheimer's disease,” Clin. Neurophysiol, vol. 116, pp. 708–715, 2005. View at: Google Scholar
  11. C. J. Stam, B. F. Jones, G. Nolte, M. Breakspear, and P. Scheltens, “Small-world networks and functional connectivity in Alzheimer's disease,” Cerebral Cortex, vol. 17, pp. 92–99, 2007. View at: Google Scholar
  12. V. Jelic and J. Kowalski, “Evidence-based evaluation of diagnostic accuracy of resting EEG in dementia and mild cognitive impairment,” Clin. EEG Neurosci, vol. 40, pp. 129–142, 2009. View at: Google Scholar
  13. Sneddon, R. W. Shankle, J. Hara, A. Rodriguez, D. Horrman, and U. Saha, “EEG detection of early Alzheimer's disease using psychophysical tasks,” Clin. EEG Neurosci, vol. 36, no. 3, pp. 141–150, 2005. View at: Google Scholar
  14. P. Zhao, P. Van-Eetvelt, C. Goh, N. Hudson, S. Wimalaratna, and E. Ifeachor, “Characterization of EEGs in Alzheimer's disease using information theoretic methods,” in Proc. IEEE Eng. Med. Biol. Soc, 2007: 512705131. View at: Google Scholar
  15. J. McBride, X. Zhao, N. Munro, C. Smith, G. Jicha, and Y. Jiang, “Resting EEG discrimination of early stage Alzheimer's disease from normal aging using inter-channel coherence network graphs,” Ann. Biomed. Eng, vol. 41, pp. 1233–1242, 2013. View at: Google Scholar
  16. J. McBride, X. Zhao, T. Nichols et al., “Scalp EEG-based discrimination of cognitive deficits after traumatic brain injury using event-related Tsallis entropy analysis,” IEEE Trans. Biomed. Eng, vol. 60, pp. 90–96, 2013. View at: Google Scholar
  17. J. McBride, X. Zhao, N. Munro et al., “Sugihara causality analysis of scalp EEG for detection of early Alzheimer's disease,” NeuroImage: Clinical, vol. 7, pp. 258–265, 2015. View at: Google Scholar
  18. J. McBride, X. Zhao, N. Munro et al., “Spectral and complexity analysis of scalp EEG characteristics for mild cognitive impairment and early Alzheimer's disease,” Comput. Meth. Prog. Bio, vol. 114, pp. 153–163, 2014. View at: Google Scholar
  19. F. Schmitt, P. Nelson, E. Abner et al., “University of Kentucky Sanders-Brown healthy brain aging volunteers: donor characteristics, procedures and neuropathology,” Curr. Alzheimer Res, vol. 9, pp. 724–733, 2012. View at: Google Scholar
  20. R. Sneddon, “The Tsallis entropy of natural information,” Physica A, vol. 386, no. 1, pp. 101–118, 2007. View at: Google Scholar
  21. S. Kullback, Information Theory and Statistics, Wiley, New York, NY, 1959.
  22. J. T. Lizier and M. Prokopenko, “Differentiating information transfer and causal effect,” European Physical Journal B, vol. 73, pp. 605–615, 2010. View at: Google Scholar
  23. M. Wibral, N. Pampu, V. Priesemann, F. Siebenhühner, H. Seiwert et al., “Measuring Information-Transfer Delays,” PLoS ONE, vol. 8, no. 2, Article ID e55809, 2013. View at: Google Scholar
  24. MathWorks, “MATLAB,” Accessed Feb. 2013, Available at www.mathworks.com/products/matlab/. View at: Google Scholar
  25. T. Hastie and R. Tibshirani, “Classification by pairwise coupling,” Ann. Statist, vol. 26, pp. 451–471, 1998. View at: Google Scholar
  26. C. Bishop, “Neural Networks for Pattern Recognition,” pp. 295-329, Oxford Univ. Press, 2008. View at: Google Scholar
  27. T. Nowotny, “Two challenges of correct validation in pattern recognition,” Frontiers in Robotics and AI, vol. 1, Article ID 5, pp. 1–6, 2014. View at: Google Scholar
  28. T. Nichols and A. Holmes, “Nonparametric permutation tests for functional neuroimaging: a primer with examples,” Hum. Brain. Map, vol. 15, pp. 1–25, 2001. View at: Google Scholar
  29. F. Vecchio, C. Babiloni, R. Lizio et al., “Resting state cortical EEG rhythms in Alzheimer's disease: toward EEG markers for clinical applications: a review,” Suppl Clin Neurophysiol, vol. 62, pp. 223–236, 2013. View at: Google Scholar
  30. J. M. Olichney, J. Pak, D. P. Salmon, J. C. Yang, T. Gahagan, R. Nowacki et al., “Abnormal P600 word repetition effect in elderly persons with preclinical Alzheimer's disease,” Cognitive Neuroscience, vol. 1, no. 1, pp. 1–9, 2013. View at: Google Scholar
  31. M. Baker, K. Akrofi, R. Schiffer, and M. W. O'Boyle, “EEG patterns in mild cognitive impairment (MCI) patients,” Open Neuroimag J, vol. 2, pp. 52–55, 2008. View at: Google Scholar
  32. C. Huang, L. Wahlund, T. Dierks, P. Julin, B. Winblad, and V. Jelic, “Discrimination of Alzheimer's disease and mild cognitive impairment by equivalent EEG sources: a cross-sectional and longitudinal study,” Clin. Neurophysiol, vol. 111, pp. 1961–1967, 2000. View at: Google Scholar
  33. K. Iqbal, A. C. Alonso, S. Chen et al., “Tau pathology in Alzheimer's disease and other tauopathies,” Biochim. Biophys. Acta, vol. 1793, pp. 198–210, 2005. View at: Google Scholar
  34. P. M. Rossini, C. Del Percio, P. Pasqualetti et al., “Conversion from mild cognitive impairment to Alzheimer's disease is predicted by source and coherence of brain electroencephalography rhythms,” Neuroscience, vol. 143, pp. 793–803, 2006. View at: Google Scholar
  35. B. Gold, D. K. Powell, X. Liang, Y. Jiang, and P. A. Hardy, “Speed of lexical decision correlates with diffusion anisotropy in left parietal and frontal white matter: evidence from diffusion tensor imaging,” Neuropsychologia, vol. 45, pp. 2439–2446, 2007. View at: Google Scholar
  36. J. Hertze, S. Palmqvist, L. Minthon, and O. Hansson, “Tau Pathology and Parietal White Matter Lesions Have Independent but Synergistic Effects on Early Development of Alzheimer's Disease,” Dementia and Geriatric Cognitive Disorders Extra, vol. 3, no. 1, pp. 113–122, 2013. View at: Google Scholar
  37. D. Lake, J. Richmann, M. Griffin, and J. Moorman, “Sample entropy analysis of neonatal heart rate variability,” Am. J. Physiol, vol. 283, no. 3, pp. R789–R797, 2002. View at: Google Scholar

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