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

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.65.1331Search 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

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.38.3017Search in Google Scholar

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-USearch in Google Scholar

Alexandersson, A., Steingrimsdottir, T., Terrien, J., Marque, C., & Karlsson, B. (2015). The Icelandic 16-electrode electrohysterogram database. Scientific Data, 2, 150017.10.1038/sdata.2015.17Search in Google Scholar

Bassingthwaighte, J. B., Liebovitch, L. S., & West, B. J. (2013). Fractal physiology. Springer.Search in Google Scholar

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.Search in Google Scholar

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-USearch in Google Scholar

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.45.7073Search in Google Scholar

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)00118-8Search in Google Scholar

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-USearch in Google Scholar

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.2014.02.002Search in Google Scholar

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.2015.7319316Search in Google Scholar

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.2012.6346113Search in Google Scholar

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.93.042218Search in Google Scholar

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.2008.09.008Search in Google Scholar

Fraser, A. M., & Swinney, H. L. (1986). Independent coordinates for strange attractors from mutual information. Physical Review A, 33(2), 1134.10.1103/PhysRevA.33.1134Search in Google Scholar

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.e215Search in Google Scholar

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.Search in Google Scholar

Grassberger, P., & Procaccia, I. (1983). Characterization of strange attractors. Physical Review Letters, 50(5), 346–349.10.1103/PhysRevLett.50.346Search in Google Scholar

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.Search in Google Scholar

Jaśkowski, P. (1995). Zastosowanie metod dynamiki nieliniowej do analizy sygnału EEG człowieka. Current Topics in Biophysics, 19, 42–57.Search in Google Scholar

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.Search in Google Scholar

Korus, L., & Piorek, M. (2015). Compound method of time series classification. Nonlinear Analysis, Modelling and Control, 20(4), 545–560.10.15388/NA.2015.4.6Search in Google Scholar

Lainscsek, C., & Sejnowski, T. J. (2015). Delay differential analysis of time series. Neural computation, 27(3), 594–614.10.1162/NECO_a_00706Search in Google Scholar

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).Search in Google Scholar

Liebert, W., Pawelzik, K., & Schuster, H. (1991). Optimal embeddings of chaotic attractors from topological considerations. EPL (Europhysics Letters), 14(6), 521–526.10.1209/0295-5075/14/6/004Search in Google Scholar

Marwan, N. (2014). Cross Recurrence Plot Toolbox for Matlab, Ver.5.15, Release 28.10.2015Search in Google Scholar

Ouyang, G., & Li, X. (2016). Dynamical Similarity Analysis of EEG Recordings Signal Processing in Neuroscience (pp. 111–124). Singapore: Springer.Search in Google Scholar

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.45.712Search in Google Scholar

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.Search in Google Scholar

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_40Search in Google Scholar

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.1995.1027Search in Google Scholar

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_139Search in Google Scholar

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.Search in Google Scholar

Radomski, D. S. (2015). A nonlinear parameterization of multivariate electrohysterographical signals. Computers in Biology and Medicine, 67, 13–20.10.1016/j.compbiomed.2015.10.005Search in Google Scholar

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.Search in Google Scholar

Takens, F. (1981). Detecting strange attractors in turbulence. Dynamical systems and turbulence, Warwick 1980 (pp. 366–381). Springer-Verlag Berlin Heidelberg.10.1007/BFb0091924Search in Google Scholar

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.0000000057Search in Google Scholar