Assessment of Multiscale Information for Short Physiological Time Series

This paper presents a multiscale information measure of
Electroencephalogram (EEG) for analysis with a short data length.
A multiscale extension of permutation entropy (MPE) is capable of
fully reflecting the dynamical characteristics of EEG across different
temporal scales. However, MPE yields an imprecise estimation due
to coarse-grained procedure at large scales. We present an improved
MPE measure to estimate entropy more accurately with a short
time series. By computing entropies of all coarse-grained time series
and averaging those at each scale, it leads to the modified MPE
(MMPE) which provides an enhanced accuracy as compared to
MPE. Simulation and experimental studies confirmed that MMPE
has proved its capability over MPE in terms of accuracy.

Authors:

• Young-Seok Choi

Keywords:

• Multiscale entropy
• permutation entropy
• EEG
• seizure.

References:

[1] R. Schuyler, A. White, K. Staley, and K. J. Cios, “Epileptic seizure
detection,” Engineering in Medicine and Biology Magazine, IEEE,
vol. 26, no. 2, pp. 74–81, 2007.
[2] H. Adeli, Z. Zhou, and N. Dadmehr, “Analysis of eeg records in
an epileptic patient using wavelet transform,” Journal of neuroscience
methods, vol. 123, no. 1, pp. 69–87, 2003.
[3] S. Ghosh-Dastidar, H. Adeli, and N. Dadmehr, “Mixed-band
wavelet-chaos-neural network methodology for epilepsy and epileptic
seizure detection,” Biomedical Engineering, IEEE Transactions on,
vol. 54, no. 9, pp. 1545–1551, 2007.
[4] C. E. Shannon, “Communication theory of secrecy systems*,” Bell
system technical journal, vol. 28, no. 4, pp. 656–715, 1949.
[5] V. Srinivasan, C. Eswaran, and N. Sriraam, “Approximate entropy-based
epileptic eeg detection using artificial neural networks,” Information
Technology in Biomedicine, IEEE Transactions on, vol. 11, no. 3, pp.
288–295, 2007.
[6] N. Kannathal, M. L. Choo, U. R. Acharya, and P. Sadasivan, “Entropies
for detection of epilepsy in eeg,” Computer methods and programs in
biomedicine, vol. 80, no. 3, pp. 187–194, 2005.
[7] C. Bandt and B. Pompe, “Permutation entropy: a natural complexity
measure for time series,” Physical Review Letters, vol. 88, no. 17, p.
174102, 2002.
[8] N. Nicolaou and J. Georgiou, “The use of permutation entropy
to characterize sleep electroencephalograms,” Clinical EEG and
Neuroscience, vol. 42, no. 1, pp. 24–28, 2011.
[9] X. Li, G. Ouyang, and D. A. Richards, “Predictability analysis of
absence seizures with permutation entropy,” Epilepsy research, vol. 77,
no. 1, pp. 70–74, 2007.
[10] M. Costa, A. L. Goldberger, and C.-K. Peng, “Multiscale entropy
analysis of complex physiologic time series,” Physical review letters,
vol. 89, no. 6, p. 068102, 2002.
[11] W. Aziz and M. Arif, “Multiscale permutation entropy of physiological
time series,” in 9th International Multitopic Conference, IEEE INMIC
2005. IEEE, 2005, pp. 1–6.
[12] J. M. Amig´o, S. Zambrano, and M. A. Sanju´an, “True and false
forbidden patterns in deterministic and random dynamics,” EPL
(Europhysics Letters), vol. 79, no. 5, p. 50001, 2007.
[13] R. G. Andrzejak, K. Lehnertz, F. Mormann, C. Rieke, P. David, and C. E.
Elger, “Indications of nonlinear deterministic and finite-dimensional
structures in time series of brain electrical activity: Dependence on
recording region and brain state,” Physical Review E, vol. 64, no. 6,
p. 061907, 2001.