Permutation Entropy of the Electroencephalogram Background Activity in Alzheimer’s Disease - Investigation into the Incidence of Repeated Values

Samantha Simons, Daniel Abásolo

2014

Abstract

This pilot study applied Permutation Entropy (PE), a non-linear symbolic measure, and a novel modification (modPE), to investigate the regularity of electroencephalogram (EEG) signals from 11 Alzheimer’s disease (AD) patients and 11 age-matched controls given input parameters n (embedding vector), τ (coarse graining) and slide (difference between the start of two concurrent embedding vectors). PE discriminated better than modPE with controls showing reduced regularity over AD patients. Increasing τ identified the greatest differences between EEG signals. Longer embedding vectors were also more able to identify differences. The greatest difference between groups was at Fp1 with n,τ,slide = 3,10,1 (p=0.0112 Kruskal Wallis with Bonferroni). Subject and epoch based leave-one-out cross validation was carried out with thresholding from Receiver Operating Characteristic Curves. The greatest ability to correctly identify AD patients and controls were 81.82% (Fp2 n,τ,slide = 7,4,4, PE and modPE, F7 n,τ,slide = 3,10,1, PE and modPE) and 90.91% (Fp1 n,τ,slide = 3,10,1, PE and modPE), respectively. The maximum accuracy (both groups correctly identified) was 81.82% seen at many electrode and input combinations. All are with subject based analysis. This suggests that PE can identify changes in EEG signals in AD, given appropriate variables. However, modPE makes little improvement over PE.

References

  1. Abásolo, D., Hornero, R., Gómez, C., García, M., Lopez, M., 2006 Analysis of EEG background activity in Alzheimer's disease patients with Lempel-Ziv complexity and central tendency measure. Medical Engineering and Physics, 28, pp. 315-322.
  2. Bandt, C., Pompe, B., 2002. Permutation entropy- a natural complexity measure for time series. Physical Review Letters, 88(17), 174102.
  3. Bian, C., Qin, C., Ma, Q. D. Y., Shen, Q., 2012. Modified permutation-entropy analysis of heartbeat dynamics. Physical Review E, 85, 021906.
  4. Cao, Y., Tung, W., Gao, J. B., Protopopescu, V. A., Hively, L. M., 2004. Detecting dynamical changes in time series using the permutation entropy. Physical Review E, 70, 046217.
  5. Dauwels, J., Srinivasan, K., Ramasubba Reddy, M., Musha, T., Vialatte, F.-B., Latchoumane, C., Jeong, J., Cichocki, A., 2011. Slowing and loss of complexity in Alzheimer's EEG: Two sides of the same coin?. International Journal of Alzheimer's Disease, 2011, 539621.
  6. Dauwels, J., Vialatte, F., Cicjocki, A., 2010. Diagnosis of Alzheimer's disease from EEG signals: Where are we standing?. Current Alzheimer's Research, 7, pp. 487- 505.
  7. Escudero, J., Abásolo, D., Hornero, R., Espino, P., López, M., 2006. Analysis of electroencephalograms in Alzheimer's disease patients with multiscale entropy. Physiological Measurement, 27, pp. 1091-1106.
  8. Fadlallah, B., Chen, B., Keil, A., Príncipe, J., 2013. Weighted-permutation entropy: A complexity measure for time series incorporating amplitude information. Physical Review E, 87, pp. 022911.
  9. Fawcett, T., 2006. An introduction to ROC analysis. Pattern Recognition Letters, 27, pp. 861-874.
  10. Folstein, M. F., Folstein, S. E., McHugh, P. R., 1975. Mini-mental state. A practical method for grading the cognitive state of patients for the clinician. American Journal of Physiology: Heart and Circulatory Physiology, 12, pp. 189-198.
  11. Li, D., Liang, Z., Wang, Y., Hagihira, S., Sleigh, J. W., Li, X., 2013. Parameter selection in permutation entropy for an electroencephalographic measure of isoflurane anesthetic drug effect. Journal of Clinical Monitoring and Computing, 27, pp. 113-123.
  12. Morabito, G., Bramanti, A., Labate, D., la Foresta, F., Morabito, F.C., 2011. Early detection of Alzheimer's onset with permutation entropy analysis of EEG. Natural Intelligence: the INNS Magazine, 1(1), pp. 30- 32.
  13. Morabito, F. C., Labate, D., la Foresta, F., Bramanti, A., Morabito, G., Palamara, I., 2012. Multivariate multiscale permutation entropy for complexity analysis of Alzheimer's disease EEG. Entropy, 14, pp. 1186- 1202.
  14. Pievani, M., de Haan, W., Wu, T., Seeley, W. W., Frisoni, G. B., 2011. Functional network disruption in the degenerative dementias. Lancet Neurology, 10, pp. 829-843.
  15. Riedl, M., Müller, A., Wessel, N., 2013. Practical considerations of permutation entropy. The European Physical Journal Special Topics, 222, pp. 249-262.
  16. Shannon, C. E., 1948. A mathematical theory of communication. The Bell System Technical Journal, 27, pp. 379-423.
  17. Simons, S., Abásolo, D., Escudero, J., 2012a. Quadratic sample entropy and multiscale quadratic sample entropy of the electroencephalogram in Alzheimer's disease. In Proceedings of the 5th International Conference on Medical Signals and Information Processing.
  18. Simons, S., Abásolo, D., Escudero, J., 2012b. Fuzzy entropy and multiscale fuzzy entropy of the electroencephalogram in Alzheimer's disease. In Proceedings of the Young Researchers Futures Meeting-Neural Engineering, Royal Academy of Engineering.
  19. Xiao-Feng, L., Yue, W., 2009. Fine-grained permutation entropy as a measure of natural complexity for time series. Chinese Physics B, 18(7), pp. 2690-2695.
  20. Zanin, M., Zunino, L., Rosso, O. A., Papo, D., 2012. Permutation entropy and its main biomedical and econophysics applications: A review. Entropy, 14, pp. 1553-1577.
Download


Paper Citation


in Harvard Style

Simons S. and Abásolo D. (2014). Permutation Entropy of the Electroencephalogram Background Activity in Alzheimer’s Disease - Investigation into the Incidence of Repeated Values . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2014) ISBN 978-989-758-011-6, pages 97-103. DOI: 10.5220/0004721000970103


in Bibtex Style

@conference{biosignals14,
author={Samantha Simons and Daniel Abásolo},
title={Permutation Entropy of the Electroencephalogram Background Activity in Alzheimer’s Disease - Investigation into the Incidence of Repeated Values},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2014)},
year={2014},
pages={97-103},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004721000970103},
isbn={978-989-758-011-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2014)
TI - Permutation Entropy of the Electroencephalogram Background Activity in Alzheimer’s Disease - Investigation into the Incidence of Repeated Values
SN - 978-989-758-011-6
AU - Simons S.
AU - Abásolo D.
PY - 2014
SP - 97
EP - 103
DO - 10.5220/0004721000970103