Commenced in January 2007
Paper Count: 30375
Assessment of Multiscale Information for Short Physiological Time Series
Authors: Young-Seok Choi
Abstract: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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1111937Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1079
 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.
 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.
 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.
 C. E. Shannon, “Communication theory of secrecy systems*,” Bell system technical journal, vol. 28, no. 4, pp. 656–715, 1949.
 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.
 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.
 C. Bandt and B. Pompe, “Permutation entropy: a natural complexity measure for time series,” Physical Review Letters, vol. 88, no. 17, p. 174102, 2002.
 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.
 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.
 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.
 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.
 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.
 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.