Have a personal or library account? Click to login
Selection of Phase Space Reconstruction Parameters for EMG Signals of the Uterus Cover

Selection of Phase Space Reconstruction Parameters for EMG Signals of the Uterus

Open Access
|Jan 2017

References

  1. Abarbanel, H. D., Brown, R., Sidorowich, J. J., & Tsimring, L. S. (1993). The analysis of observed chaotic data in physical systems. Reviews of Modern Physics, 65(4), 1331.10.1103/RevModPhys.65.1331
  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. Albano, A., Muench, J., Schwartz, C., Mees, A., & Rapp, P. (1988). Singular-value decomposition and the Grassberger-Procaccia algorithm. Physical Review A, 38(6), 3017.10.1103/PhysRevA.38.3017
  4. Albano, A., Passamante, A., & Farrell, M. E. (1991). Using higher-order correlations to define an embedding window. Physica D: Nonlinear Phenomena, 54(1–2), 85–97.10.1016/0167-2789(91)90110-U
  5. Alexandersson, A., Steingrimsdottir, T., Terrien, J., Marque, C., & Karlsson, B. (2015). The Icelandic 16-electrode electrohysterogram database. Scientific Data, 2, 150017.10.1038/sdata.2015.17
  6. Bassingthwaighte, J. B., Liebovitch, L. S., & West, B. J. (2013). Fractal physiology. Springer.
  7. Broomhead, D., & Jones, R. (1989). Time-series analysis. Paper presented at the Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences.
  8. Buzug, T., & Pfister, G. (1992a). Comparison of algorithms calculating optimal embedding parameters for delay time coordinates. Physica D: Nonlinear Phenomena, 58(1), 127–137.10.1016/0167-2789(92)90104-U
  9. Buzug, T., & Pfister, G. (1992b). Optimal delay time and embedding dimension for delay-time coordinates by analysis of the global static and local dynamical behavior of strange attractors. Physical Review A, 45(10), 7073–7084.10.1103/PhysRevA.45.7073
  10. Cao, L. (1997). Practical method for determining the minimum embedding dimension of a scalar time series. Physica D: Nonlinear Phenomena, 110(1), 43–50.10.1016/S0167-2789(97)00118-8
  11. Casdagli, M., Eubank, S., Farmer, J. D., & Gibson, J. (1991). State space reconstruction in the presence of noise. Physica D: Nonlinear Phenomena, 51(1), 52–98.10.1016/0167-2789(91)90222-U
  12. Chen, M., Fang, Y., & Zheng, X. (2014). Phase space reconstruction for improving the classification of single trial EEG. Biomedical Signal Processing and Control, 11, 10–16.10.1016/j.bspc.2014.02.002
  13. Diab, A., Falou, O., Hassan, M., Karlsson, B., & Marque, C. (2015). Effect of filtering on the classification rate of nonlinear analysis methods applied to uterine EMG signals. Paper presented at the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).10.1109/EMBC.2015.7319316
  14. Diab, A., Hassan, M., Marque, C., & Karlsson, B. (2012). Quantitative performance analysis of four methods of evaluating signal nonlinearity: application to uterine EMG signals. Paper presented at the Annual International Conference of the IEEE Engineering in Medicine and Biology Society.10.1109/EMBC.2012.6346113
  15. Erem, B., Orellana, R. M., Hyde, D. E., Peters, J. M., Duffy, F. H., Stovicek, P., Warfield, S. K., et al. (2016). Extensions to a manifold learning framework for time-series analysis on dynamic manifolds in bioelectric signals. Physical Review E, 93(4), 042218.10.1103/PhysRevE.93.042218
  16. Euliano, T. Y., Marossero, D., Nguyen, M. T., Euliano, N. R., Principe, J., & Edwards, R. K. (2009). Spatiotemporal electrohysterography patterns in normal and arrested labor. American Journal of Obstetrics and Gynecology, 200(1), 54.e1–54.e7.10.1016/j.ajog.2008.09.008
  17. Fraser, A. M., & Swinney, H. L. (1986). Independent coordinates for strange attractors from mutual information. Physical Review A, 33(2), 1134.10.1103/PhysRevA.33.1134
  18. Goldberger, A. L., Amaral, L. A., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., Mietus, et al. (2000). Physiobank, physiotoolkit, and physionet. Components of a new research resource for complex physiologic signals. Circulation, 101(23), e215–e220.10.1161/01.CIR.101.23.e215
  19. Graczyk, S., Horoba, K., Jerewski, J., Wrobel, J., & Gacek, A. (1998). Use of running statistical evaluation in analysis of electrohysterographic signals. Paper presented at the 8th Proceedings of the Mediterranean Conference on Medical and Biological Engineering and Computing.
  20. Grassberger, P., & Procaccia, I. (1983). Characterization of strange attractors. Physical Review Letters, 50(5), 346–349.10.1103/PhysRevLett.50.346
  21. Huffaker, R. (2010). Phase Space Reconstruction from Time Series Data: Where History Meets Theory. Proceedings in System Dynamics and Innovation in Food Networks 2010, 1–9.
  22. Jaśkowski, P. (1995). Zastosowanie metod dynamiki nieliniowej do analizy sygnału EEG człowieka. Current Topics in Biophysics, 19, 42–57.
  23. Kliková, B., & Raidl, A. (2011). Reconstruction of phase space of dynamical systems using method of time delay. WDS’11 Proceedings of Contributed Papers: Part III – Physics, pp. 83–87.
  24. Korus, L., & Piorek, M. (2015). Compound method of time series classification. Nonlinear Analysis, Modelling and Control, 20(4), 545–560.10.15388/NA.2015.4.6
  25. Lainscsek, C., & Sejnowski, T. J. (2015). Delay differential analysis of time series. Neural computation, 27(3), 594–614.10.1162/NECO_a_00706
  26. Legg, P. A., Rosin, P. L., Marshall, D., & Morgan, J. E. (2007). Improving accuracy and efficiency of registration by mutual information using Sturges’ histogram rule. Paper presented at the 11th Annual Conference in Medical Image Understanding and Analysis (pp. 26–30).
  27. Liebert, W., Pawelzik, K., & Schuster, H. (1991). Optimal embeddings of chaotic attractors from topological considerations. EPL (Europhysics Letters), 14(6), 521–526.10.1209/0295-5075/14/6/004
  28. Marwan, N. (2014). Cross Recurrence Plot Toolbox for Matlab, Ver.5.15, Release 28.10.2015
  29. Ouyang, G., & Li, X. (2016). Dynamical Similarity Analysis of EEG Recordings Signal Processing in Neuroscience (pp. 111–124). Singapore: Springer.
  30. Packard, N. H., Crutchfield, J. P., Farmer, J. D., & Shaw, R. S. (1980). Geometry from a Time Series. Physical Review Letters, 45(9), 712.10.1103/PhysRevLett.45.712
  31. Palit, S. K., Mukherjee, S., Banerjee, S., Ariffin, M., & Bhattacharya, D. (2015). Some Time-Delay Finding Measures and Attractor Reconstruction Applications of Chaos and Nonlinear Dynamics in Science and Engineering – Vol. 4 Understanding Complex Systems (pp. 215–256). Switzerland: Springer.
  32. Piórek, M. (2016). Mutual Information for Quaternion Time Series. Paper presented at the IFIP International Conference on Computer Information Systems and Industrial Management.10.1007/978-3-319-45378-1_40
  33. Pritchard, W. S., & Duke, D. W. (1995). Measuring chaos in the brain – a tutorial review of EEG dimension estimation. Brain and Cognition, 27(3), 353–397.10.1006/brcg.1995.1027
  34. Przybyła, T., Pander, T., Wróbel, J., Czabański, R., Roj, D., & Matonia, A. (2014). A recovery of FHR signal in the Embedded Space. Paper presented at the XIII Mediterranean Conference on Medical and Biological Engineering and Computing.10.1007/978-3-319-00846-2_139
  35. Radomski, D. S. (2014). A mulltivariate sample entropy of differentiated electtrohysterographical signals for an identification of an Uterine Labor Activity. Information Technology in Biomedicine, 4, 303–310.
  36. Radomski, D. S. (2015). A nonlinear parameterization of multivariate electrohysterographical signals. Computers in Biology and Medicine, 67, 13–20.10.1016/j.compbiomed.2015.10.005
  37. Ruelle, D. (1990). The Claude Bernard Lecture, 1989. Deterministic chaos: the science and the fiction. Paper presented at the Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences.
  38. Takens, F. (1981). Detecting strange attractors in turbulence. Dynamical systems and turbulence, Warwick 1980 (pp. 366–381). Springer-Verlag Berlin Heidelberg.10.1007/BFb0091924
  39. Xia, D.-H., Song, S.-Z., & Behnamian, Y. (2016). Detection of corrosion degradation using electrochemical noise (EN): review of signal processing methods for identifying corrosion forms. Corrosion Engineering Science and Technology, 51(7), 527–544.10.1179/1743278215Y.0000000057
DOI: https://doi.org/10.1515/slgr-2016-0046 | Journal eISSN: 2199-6059 | Journal ISSN: 0860-150X
Language: English
Page range: 47 - 59
Published on: Jan 23, 2017
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Related subjects:

© 2017 Ewelina Brzozowska, Marta Borowska, published by University of Białystok
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.