Empirical Mode Decomposition Based Multiscale Analysis of Physiological Signal
We present a refined multiscale Shannon entropy for
analyzing electroencephalogram (EEG), which reflects the underlying
dynamics of EEG over multiple scales. The rationale behind
this method is that neurological signals such as EEG possess
distinct dynamics over different spectral modes. To deal with the
nonlinear and nonstationary nature of EEG, the recently developed
empirical mode decomposition (EMD) is incorporated, allowing a
decomposition of EEG into its inherent spectral components, referred
to as intrinsic mode functions (IMFs). By calculating the Shannon
entropy of IMFs in a time-dependent manner and summing them over
adaptive multiple scales, it results in an adaptive subscale entropy
measure of EEG. Simulation and experimental results show that
the proposed entropy properly reveals the dynamical changes over
multiple scales.
[1] G. Buzs´aki, Rhythms of the brain, Oxford: Oxford University Press,
2006.
[2] D. S. Bassett, and E. T. Bullmore, “Human brain networks in health
and disease,” Current Opinion in Neuro., vol. 22, pp. 340–347, 2009. [3] L. S. Prichep, A. Jacquin, J. Filipenko, S. G. Dastidar, S. Zabele, A.
Vodencarevic, and N. S. Rothman, “Classification of traumatic brain
injury severity using informed data reduction in a series of binary
classifier algorithms,” IEEE Trans. Neural Syst. and Rehab. Eng.,
vol. 20, no. 6, pp. 806–822, Dec. 2012.
[4] R. Ferenets, T. Lipping, A. Anier, V. J¨antti, S. Melto, and S. Hovilehto,
“Comparison of entropy and complexity measures for the assessment
of depth of sedation,” IEEE Trans. Biomed. Eng., vol. 53, no. 6,
pp. 1067–1077, June 2006.
[5] C. Cao and S. Slobounov, “Application of a novel measure of EEG
nonstationarity as Shannon-entropy of the peak frequency shifting
for detecting residual abnormalities in concussed individuals,” Clin.
Neurophysilo., vol. 122, no. 7, pp. 1314–1321, Jul. 2011.
[6] C. Shannon, “A mathematical theory of communication,” Bell Syst.
Tech. J., vol. 27, pp. 379-423, 1948.
[7] N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, and Q.
Zheng, “The empirical mode decomposition and the Hilbert spectrum
for nonlinear and non-stationary time series analysis,” Proceedings:
Mathematical, Physical and Engineering Sciences., vol. 454,
pp 903–995, 1998.
[8] H. Liang, S. L. Bressler, R. Desimone, and P. Fries, “Empirical mode
decomposition: a method for analyzing neural data,” Neurocomputing,
vol. 65, pp. 801–807, 2005.
[9] C. M. Sweeney-Reed and S. J. Nasuto, “A novel approach to
the detection of synchronisation in EEG based on empirical mode
decomposition,” J. Comput. Neurosci,, vol. 23, no. 1, pp. 79–111,
Aug. 2007.
[10] Y.-S. Choi, M. Koenig, X. Jia, and N. Thakor, “Quantifying time-varying
multiunit neural activity using entropy-based measures,” IEEE Trans.
Biomed. Eng., vol. 57, no. 11, pp. 2771–2777, Nov. 2010.
[11] L. Katz, U. Ebmeyer, P. Safar, A. Radovsky, and R. Neumar, “Outcome
model of asphyxial cardiac arrest in rats,” J. Cereb. Blood Flow Metab.,
vol. 15, pp. 1032-1039, Nov. 1995.
[1] G. Buzs´aki, Rhythms of the brain, Oxford: Oxford University Press,
2006.
[2] D. S. Bassett, and E. T. Bullmore, “Human brain networks in health
and disease,” Current Opinion in Neuro., vol. 22, pp. 340–347, 2009. [3] L. S. Prichep, A. Jacquin, J. Filipenko, S. G. Dastidar, S. Zabele, A.
Vodencarevic, and N. S. Rothman, “Classification of traumatic brain
injury severity using informed data reduction in a series of binary
classifier algorithms,” IEEE Trans. Neural Syst. and Rehab. Eng.,
vol. 20, no. 6, pp. 806–822, Dec. 2012.
[4] R. Ferenets, T. Lipping, A. Anier, V. J¨antti, S. Melto, and S. Hovilehto,
“Comparison of entropy and complexity measures for the assessment
of depth of sedation,” IEEE Trans. Biomed. Eng., vol. 53, no. 6,
pp. 1067–1077, June 2006.
[5] C. Cao and S. Slobounov, “Application of a novel measure of EEG
nonstationarity as Shannon-entropy of the peak frequency shifting
for detecting residual abnormalities in concussed individuals,” Clin.
Neurophysilo., vol. 122, no. 7, pp. 1314–1321, Jul. 2011.
[6] C. Shannon, “A mathematical theory of communication,” Bell Syst.
Tech. J., vol. 27, pp. 379-423, 1948.
[7] N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, and Q.
Zheng, “The empirical mode decomposition and the Hilbert spectrum
for nonlinear and non-stationary time series analysis,” Proceedings:
Mathematical, Physical and Engineering Sciences., vol. 454,
pp 903–995, 1998.
[8] H. Liang, S. L. Bressler, R. Desimone, and P. Fries, “Empirical mode
decomposition: a method for analyzing neural data,” Neurocomputing,
vol. 65, pp. 801–807, 2005.
[9] C. M. Sweeney-Reed and S. J. Nasuto, “A novel approach to
the detection of synchronisation in EEG based on empirical mode
decomposition,” J. Comput. Neurosci,, vol. 23, no. 1, pp. 79–111,
Aug. 2007.
[10] Y.-S. Choi, M. Koenig, X. Jia, and N. Thakor, “Quantifying time-varying
multiunit neural activity using entropy-based measures,” IEEE Trans.
Biomed. Eng., vol. 57, no. 11, pp. 2771–2777, Nov. 2010.
[11] L. Katz, U. Ebmeyer, P. Safar, A. Radovsky, and R. Neumar, “Outcome
model of asphyxial cardiac arrest in rats,” J. Cereb. Blood Flow Metab.,
vol. 15, pp. 1032-1039, Nov. 1995.
@article{"International Journal of Information, Control and Computer Sciences:70119", author = "Young-Seok Choi", title = "Empirical Mode Decomposition Based Multiscale Analysis of Physiological Signal", abstract = "We present a refined multiscale Shannon entropy for
analyzing electroencephalogram (EEG), which reflects the underlying
dynamics of EEG over multiple scales. The rationale behind
this method is that neurological signals such as EEG possess
distinct dynamics over different spectral modes. To deal with the
nonlinear and nonstationary nature of EEG, the recently developed
empirical mode decomposition (EMD) is incorporated, allowing a
decomposition of EEG into its inherent spectral components, referred
to as intrinsic mode functions (IMFs). By calculating the Shannon
entropy of IMFs in a time-dependent manner and summing them over
adaptive multiple scales, it results in an adaptive subscale entropy
measure of EEG. Simulation and experimental results show that
the proposed entropy properly reveals the dynamical changes over
multiple scales.", keywords = "EEG, subscale entropy, Empirical mode
decomposition, Intrinsic mode function.", volume = "9", number = "5", pages = "1332-4", }