Have a personal or library account? Click to login

Entropy-Based Algorithms in the Analysis of Biomedical Signals

Open Access
|Jan 2016

References

  1. Akareddy, S. M., & Kulkarni, P. K. (2013). EEG signal classification for epilepsy seizure detection using improved approximate entropy. International Journal of Public Health Science (IJPHS), 2(1), 23–32.10.11591/ijphs.v2i1.1836
  2. Alamedine, D., Diab, A., Muszynski, C., Karlsson, B., Khalil, M., & Marque, C. (2014). Selection algorithm for parameters to characterize uterine EHG signals for the detection of preterm labor. Signal, Image and Video Processing, 8(6), 1169–1178.10.1007/s11760-014-0655-2
  3. Avilov, O., Popov, A., Kanaikin, O., & Kyselova, O. (2012). Permutation Entropy Analysis of Electroencephalogram. Signal, 100, 200.
  4. Bandt, C., & Pompe, B. (2002). Permutation entropy: a natural complexity measure for time series. Physical review letters, 88(17), 174102.10.1103/PhysRevLett.88.174102
  5. Ben-Naim, A. (2008). A Farewell to Entropy: Statistical Thermodynamics Based on Information. S. World Scientific.
  6. Boltzmann, L. (1896). Vorlesungen über Gastheorie (Vol. 1). Leipzig: J. A. Barth.
  7. Chen, W., Wang, Z., Xie, H., & Yu, W. (2007). Characterization of surface EMG signal based on fuzzy entropy. Neural Systems and Rehabilitation Engineering, IEEE Transactions on, 15(2), 266–272.10.1109/TNSRE.2007.897025
  8. Clausius, R. (1850). On the motive power of heat, and on the laws which can be deduced from it for the theory of heat. Poggendorff’s Annalen Der Physick, LXXIX, 368, 500.
  9. Cornforth, D. J., Tarvainen, M. P., & Jelinek, H. F. (2013, July). Using renyi entropy to detect early cardiac autonomic neuropathy. In Engineering in Medicine and Biology Society (EMBC), 35th Annual International Conference of the IEEE (pp. 5562–5565).10.1109/EMBC.2013.6610810
  10. Diab, A., Hassan, M., Marque, C., & Karlsson, B. (2014). Performance analysis of four nonlinearity analysis methods using a model with variable complexity and application to uterine EMG signals. Medical engineering & physics, 36(6), 761–767.10.1016/j.medengphy.2014.01.009
  11. Ferlazzo, E., Mammone, N., Cianci, V., Gasparini, S., Gambardella, A., Labate, A., Aguglia, U., et al. (2014). Permutation entropy of scalp EEG: A tool to investigate epilepsies: Suggestions from absence epilepsies. Clinical Neurophysiology, 125(1), 13–20.10.1016/j.clinph.2013.06.023
  12. Frank, B., Pompe, B., Schneider, U., & Hoyer, D. (2006). Permutation entropy improves fetal behavioural state classification based on heart rate analysis from biomagnetic recordings in near term fetuses. Medical and Biological Engineering and Computing, 44(3), 179–187.10.1007/s11517-005-0015-z
  13. Fusheng, Y., Bo, H., & Qingyu, T. (2001). Approximate entropy and its application to biosignal analysis. Nonlinear Biomedical Signal Processing: Dynamic Analysis and Modeling, 2, 72–91.
  14. Garcia-Gonzalez, M. T., Charleston-Villalobos, S., Vargas-Garcia, C., Gonzalez-Camarena, R., & Aljama-Corrales, T. (2013, July). Characterization of EHG contractions at term labor by nonlinear analysis. In Engineering in Medicine and Biology Society (EMBC), 35th Annual International Conference of the IEEE (pp. 7432–7435).10.1109/EMBC.2013.6611276
  15. Graff, B., Graff, G., & Kaczkowska, A. (2012). Entropy measures of heart rate variability for short ECG datasets in patients with congestive heart failure. Acta Physica Polonica B Proc. Suppl, 5, 153–158.10.5506/APhysPolBSupp.5.153
  16. Holzinger, A., Hörtenhuber, M., Mayer, C., Bachler, M., Wassertheurer, S., Pinho, A. J., & Koslicki, D. (2014). On entropy-based data mining. In Interactive Knowledge Discovery and Data Mining in Biomedical Informatics (pp. 209–226). Berlin Heidelberg: Springer.
  17. Humeau-Heurtier, A. (2015). The Multiscale Entropy Algorithm and Its Variants: A Review. Entropy, 17(5), 3110–3123.10.3390/e17053110
  18. Kapur, J. N., & Kesavan, H. K. (1992). Entropy optimization principles with applications. New York: Academic Press.
  19. Li, J., Yan, J., Liu, X., & Ouyang, G. (2014). Using permutation entropy to measure the changes in EEG signals during absence seizures. Entropy, 16(6), 3049–3061.10.3390/e16063049
  20. Liang, Z., Wang, Y., Sun, X., Li, D., Voss, L. J., Sleigh, J. W., Li, X., et al. (2015). EEG entropy measures in anesthesia. Frontiers in computational neuroscience, 9.10.3389/fncom.2015.00016
  21. Liu, C., Li, K., Zhao, L., Liu, F., Zheng, D., Liu, C., & Liu, S. (2013). Analysis of heart rate variability using fuzzy measure entropy. Computers in Biology and Medicine, 43(2), 100–108.10.1016/j.compbiomed.2012.11.005
  22. Oczeretko, E., Kitlas, A., Swiatecka, J., Borowska, M., & Laudanski, T. (2005). Nonlinear dynamics in uterine contractions analysis. In G. Losa, D. Merlini, T. Nonnemacher, & E. Weibel (Eds.), Fractals in Biology and Medicine (pp. 215–222). Basel: Birkhäuser Verlag.
  23. Pincus, S. M. (1991). Approximate entropy as a measure of system complexity. Proceedings of the National Academy of Sciences, 88(6), 2297–2301.10.1073/pnas.88.6.2297
  24. Rényi, A. (1970). Probability theory. In North-Holland Series in Applied Mathematics and Mechanics (Vol. 10).
  25. Richman, J. S., & Moorman, J. R. (2000). Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology-Heart and Circulatory Physiology, 278(6), H2039–H2049.10.1152/ajpheart.2000.278.6.H2039
  26. Shannon, C. E., & Weaver, W. (1949). The mathematical theory of communication. University of Illinois Press.
  27. Sharma, R., Pachori, R. B., & Acharya, U. R. (2015). Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals. Entropy, 17(2), 669–691.10.3390/e17020669
  28. Tsallis, C., Mendes, R., & Plastino, A. R. (1998). The role of constraints within generalized nonextensive statistics. Physica A: Statistical Mechanics and its Applications, 261(3), 534–554.10.1016/S0378-4371(98)00437-3
  29. Zanin, M., Zunino, L., Rosso, O. A., & Papo, D. (2012). Permutation entropy and its main biomedical and econophysics applications: a review. Entropy, 14(8), 1553–1577.10.3390/e14081553
  30. Zhang, X., & Zhou, P. (2012). Sample entropy analysis of surface EMG for improved muscle activity onset detection against spurious background spikes. Journal of Electromyography and Kinesiology, 22(6), 901–907.10.1016/j.jelekin.2012.06.005
DOI: https://doi.org/10.1515/slgr-2015-0039 | Journal eISSN: 2199-6059 | Journal ISSN: 0860-150X
Language: English
Page range: 21 - 32
Published on: Jan 6, 2016
Published by: University of Białystok
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Related subjects:

© 2016 Marta Borowska, published by University of Białystok
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.