Accesso libero

Entropy-Based Algorithms in the Analysis of Biomedical Signals



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.v2i1.1836Search in Google Scholar

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/s11760-014-0655-2Search in Google Scholar

Avilov, O., Popov, A., Kanaikin, O., & Kyselova, O. (2012). Permutation Entropy Analysis of Electroencephalogram. Signal, 100, 200.Search in Google Scholar

Bandt, C., & Pompe, B. (2002). Permutation entropy: a natural complexity measure for time series. Physical review letters, 88(17), 174102.10.1103/PhysRevLett.88.174102Search in Google Scholar

Ben-Naim, A. (2008). A Farewell to Entropy: Statistical Thermodynamics Based on Information. S. World Scientific.Search in Google Scholar

Boltzmann, L. (1896). Vorlesungen über Gastheorie (Vol. 1). Leipzig: J. A. Barth.Search in Google Scholar

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.2007.897025Search in Google Scholar

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.Search in Google Scholar

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.2013.6610810Search in Google Scholar

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.2014.01.009Search in Google Scholar

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.2013.06.023Search in Google Scholar

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-zSearch in Google Scholar

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.Search in Google Scholar

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.2013.6611276Search in Google Scholar

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.5.153Search in Google Scholar

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.Search in Google Scholar

Humeau-Heurtier, A. (2015). The Multiscale Entropy Algorithm and Its Variants: A Review. Entropy, 17(5), 3110–3123.10.3390/e17053110Search in Google Scholar

Kapur, J. N., & Kesavan, H. K. (1992). Entropy optimization principles with applications. New York: Academic Press.Search in Google Scholar

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/e16063049Search in Google Scholar

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.2015.00016Search in Google Scholar

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.2012.11.005Search in Google Scholar

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.Search in Google Scholar

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.88.6.2297Search in Google Scholar

Rényi, A. (1970). Probability theory. In North-Holland Series in Applied Mathematics and Mechanics (Vol. 10).Search in Google Scholar

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.H2039Search in Google Scholar

Shannon, C. E., & Weaver, W. (1949). The mathematical theory of communication. University of Illinois Press.Search in Google Scholar

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/e17020669Search in Google Scholar

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)00437-3Search in Google Scholar

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/e14081553Search in Google Scholar

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.2012.06.005Search in Google Scholar

Frequenza di pubblicazione:
4 volte all'anno
Argomenti della rivista:
Philosophy, other