Age-Related Changes in Probability Density Function of Pairwise Euclidean Distances between Multichannel Human EEG Signals

DOI: 10.4236/jbm.2014.24004   PDF   HTML     2,739 Downloads   3,728 Views   Citations

Abstract

The probability density functions (pdf’s) and the first order structure functions (SF’s) of the pairwise Euclidean distances between scaled multichannel human EEG signals at different time lags under hypoxia and in resting state at different ages are estimated. It is found that the hyper gamma distribution is a good fit for the empirically derived pdf in all cases. It means that only two parameters (sample mean of EEG Euclidean distances at a given time lag and relevant coefficient of variation) may be used in the approximate classification of empirical pdf’s. Both these parameters tend to increase in the first twenty years of life and tend to decrease as healthy adults getting older. Our findings indicate that such age-related dependence of these parameters looks like as age- related dependence of the total brain white matter volume. It is shown that 15 min hypoxia (8% oxygen in nitrogen) causes a significant (about 50%) decrease of the mean relative displacement EEG value that is typical for the rest state. In some sense the impact of the oxygen deficit looks like the subject getting older during short-term period.

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Trifonov, M. and Rozhkov, V. (2014) Age-Related Changes in Probability Density Function of Pairwise Euclidean Distances between Multichannel Human EEG Signals. Journal of Biosciences and Medicines, 2, 19-23. doi: 10.4236/jbm.2014.24004.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] Gao, J., Hu, J. and Tung, W. (2011) Complexity Measures of Brain Wave Dynamics. Cognitive Neurodynamics, 5, 171-182. http://dx.doi.org/10.1007/s11571-011-9151-3
[2] Masquelier, T. (2013) Neural Variability, or Lack There of. Frontiers in Computational Neuroscience, 7, 1-7. http://dx.doi.org/10.3389/fncom.2013.00007
[3] Osada, R., Funkhouser, T., Chazelle, B. and Dobkin, D. (2002) Shape Distributions. ACM Transactions on Graphics, 21, 807-832. http://dx.doi.org/10.1145/571647.571648
[4] Nikolova, N.D., Toneva-Zheynova, D., Kolev, K. and Tenekedjiev, K. (2013) Monte Carlo Statistical Tests for Identity of Theoretical and Empirical Distributions of Experimental Data. In: Chan, V., Ed., Theory and Applications of Monte Carlo Simulations, InTech, 1-26. http://dx.doi.org/10.5772/53049
[5] Suzuki, E. (1964) Hyper Gamma Distribution and Its Fitting to Rainfall Data. Papers in Meteorology and Geophysics, 15, 31-51. http://www.mri-jma.go.jp/Publish/Papers/DATA/VOL_15/15_031.pdf
[6] Izenman, A.J. (1991) Recent Developments in Nonparametric Density Estimation. Journal of the American Statistical Association, 86, 205-224. http://www.jstor.org/stable/2289732
[7] Scott, D.W. (2004) Multivariate Density Estimation and Visualization. Papers/Humboldt-Universit?t Berlin, Center for Applied Statistics and Economics (CASE), No. 2004, 16. http://hdl.handle.net/10419/22190
[8] Alfaouri, M., Daqrouq, K., Abu-Isbeih, I.N., Khalaf, E.F., Al-Qawasmi, A-R. and Al-Sawalmeh, W. (2009) Quality Evaluation of Reconstructed Biological Signals. American Journal of Applied Science, 5, 187-193. http://dx.doi.org/10.3844/ajas.2009.187.193
[9] Sowell, E.R., Peterson, B.S., Thompson, P.M., Welcome, S.E., Henkenius, A.L. and Toga, A.W. (2003) Mapping Cortical Change across the Human Life Span. Nature Neuroscience, 6, 309-315. http://dx.doi.org/10.1038/nn1008
[10] Chen, Z., Liu, M., Gross, D.W. and Beaulieu, C. (2013) Graph Theoretical Analysis of Developmental Patterns of the White Matter Network. Frontiers in Human Neuroscience, 7, 1-13. http://dx.doi.org/10.3389/fnhum.2013.00716

  
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