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Sequential separation of twin pregnancy electrocardiograms

Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
We propose to tackle the problem of maternal abdominal electric signals decomposition with a combined application of independent component analysis and projective or adaptive filtering. The developed method is employed to process the four-channel abdominal signals recorded during twin pregnancy. These signals are complicated mixtures of the maternal ECG, the ECGs of the fetal twins and noise of various origin. Although the independent component analysis cannot separate the respective signals, the proposed combination of the methods deals with this task successfully. A simulation experiment confirms high efficiency of this approach.
Rocznik
Strony
91--101
Opis fizyczny
Bibliogr. 34 poz., wykr., tab.
Twórcy
autor
  • Institute of Electronics, Silesian University of Technology, 16 Akademicka St., 44-100 Gliwice, Poland
autor
  • Institute of Electronics, Silesian University of Technology, 16 Akademicka St., 44-100 Gliwice, Poland
autor
  • Department of Computer Medical Systems, Institute of Medical Technology and Equipment, 118 Roosevelt St., 41-800 Zabrze, Poland
Bibliografia
  • [1] J. Jezewski, A. Matonia, T. Kupka, D. Roj, and R. Czabanski, “Determination of the fetal heart rate from abdominal signals: evaluation of beat-to-beat accuracy in relation to the direct fetal electrocardiogram”, Biomedical Engineering/Biomedizinische Technik 57 (5), 383-394 (2012).
  • [2] D.J. Jagannath and A.I. Selvakumar, “Issues and research on foetal electrocardiogram signal elicitation”, Biomedical Signal Processing and Control 10 (1), 224-244 (2013).
  • [3] J.H. Nagel, “Progress in fetal monitoring by improved data acquisition”, IEEE Eng. Med. Biol. Mag. 3 (3), 9-13 (1984).
  • [4] B. Widrow, J.R. Glover, J.M. McCool, J. Kaunitz, C.S. Williams, R.H. Hearn, J.R. Zeidler, E. Dong, and R.C. Goodlin, “Adaptive noise cancelling: principles and the applications”, Proc. IEEE 63 (12), 1692-1716 (1975).
  • [5] P. Bergveld, A.J. Kölling, and J.H.J. Peuscher, “Real-time fetal ECG recording”, IEEE Trans. Biomed. Eng. 33 (5), 505-509 (1986).
  • [6] D. Callaerts, B. De Moor, J. Vandewalle, and W. Sansen, “Comparison of SVD methods to extract the fetal electrocardiogram from cutaneous electrode signals”, Med. Biol. Eng. Comput. 28 (3), 217-224 (1990).
  • [7] L. De Lathauwer, B. De Moor, and J. Vandewalle, “Fetal electrocardiogram extraction by blind source subspace separation”, IEEE Trans. Biomed. Eng. 47 (5), 567-572 (2000).
  • [8] V. Zarzoso and A.K. Nandi, “Noninvasive fetal electrocardiogram extraction: blind separation versus adaptive noise cancellation”, IEEE Trans. Biomed. Eng. 48 (1), 12-18 (2001).
  • [9] M. Kotas, “Projective filtering of time-aligned beats for foetal ECG extraction”, Bull. Pol. Ac.: Tech. 55 (4), 331-339 (2007).
  • [10] P. Laguna, R. Jane, O. Meste, P.W. Poon, P. Caminal, H. Rix, and N.V. Thakor, “Adaptive filter for event-related bioelectric signals using an impulse correlated reference input: Comparison with signal averaging techniques”, IEEE Trans. Biomed. Eng. 39 (10), 1032-1044 (1992).
  • [11] T. Schreiber and D.T. Kaplan, “Nonlinear noise reduction for electrocardiograms”, Chaos 6 (1), 87-92 (1996).
  • [12] F. Takens, “Detecting strange attractors in turbulence, in: Lecture Notes in Mathematics”, Dynamical Systems and Turbulence 898 (1), 366-381 (1981).
  • [13] I.T. Jollife, Principal Component Analysis, Springer, New York, 2002.
  • [14] J.M. Leski, “Robust weighted averaging”, IEEE Trans. Biomed. Eng. 49 (8), 796-804 (2002).
  • [15] A. Momot, M. Momot, and J. Leski, “Bayesian and empirical Bayesian approach to weighted averaging of ECG signal”, Bull. Pol. Ac.: Tech. 55 (4), 341-350 (2007).
  • [16] M. Kotas, “Robust projective filtering of time-warped ECG beats”, Computer Methods and Programs in Biomedicine 92 (2), 161-172 (2008).
  • [17] R. Romo Vazquez, H. Velez-Perez, R. Ranta, V. Louis Dorr, D. Maquin, and L. Maillard, “Blind source separation, wavelet denoising and discriminant analysis for EEG artefacts and noise cancelling”, Biomedical Signal Processing and Control 7 (4), 389-400 (2012).
  • [18] R.A. Salido Ruiz, R. Ranta and V. Louis-Dorr, “EEG montage analysis in the blind source separation framework”, Biomedical Signal Processing and Control 6 (1), 77-84 (2011).
  • [19] M.A. Klados, C. Papadelis, C. Braun, and P.D. Bamidis, “REG-ICA: A hybrid methodology combining blind source separation and regression techniques for the rejection of ocular artifacts”, Biomedical Signal Processing and Control 6 (3), 291-300 (2011).
  • [20] L. Albera, A. Kachenoura, P. Comon, A. Karfoul, F. Wendling, L. Senhadji, and I. Merlet, “ICA-based EEG denoising: a comparative analysis of fifteen methods”, Bull. Pol. Ac.: Tech. 60 (3), 407-418 (2012).
  • [21] J.-F. Cardoso and A. Soulemiac, “Blind beamforming for non Gaussian signals”, IEE Proceedings-F 140 (6), 362-370 (1993).
  • [22] M. Kotas, Nonlinear Projective Filtering of ECG Signals, Silesian University Press, Gliwice, 2011, (in Polish).
  • [23] M. Kotas, J. Jezewski, A. Matonia, and T. Kupka, “Towards noise immune detection of fetal QRS complexes”, Computer Methods and Programs in Biomedicine 97 (3), 241-256 (2010).
  • [24] J. Cardoso, “Multidimensional independent component analysis”, Proc. 1998 IEEE Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP 4 (1), 1941-1944 (1998).
  • [25] M. Almeida, J. Bioucas-Dias, R. Vigario, and E. Oja, “A comparison of algorithms for separation of synchronous subspaces”, Bull. Pol. Ac.: Tech. 60 (3), 455-460 (2012).
  • [26] J.A. Van Alste, W. Van Eck, and O.E. Herrmann, “ECG baseline wander reduction using linear phase filters”, Computers and Biomedical Research 19 (5), 417-427 (1986).
  • [27] J.M. Leski and N. Henzel, “ECG baseline wander and powerline interference reduction using nonlinear filter bank”, Signal Processing 85 (4), 781-793 (2005).
  • [28] S. Hunter and S.C. Robson, “Adaptation of the maternal heart in pregnancy”, British Heart J. 68 (6), 540-543 (1992).
  • [29] M. Kuleva, A. Youssef, E. Maroni, E. Contro, G. Pilu, N. Rizzo, G. Pelusi, and T. Ghi, “Maternal cardiac function in normal twin pregnancy: a longitudinal study”, Ultrasound Obstet. Gynecol 38 (5), 575-580 (2011).
  • [30] A.L. Goldberger, L.A. Amaral, L. Glass, J.M. Hausdorff, P.Ch. Ivanov, R.G. Mark, J.E. Mietus, G.B. Moody, C.-P. Peng, and H.E. Stanley, “PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physio- logic signals”, Circulation 101 (23), e215-e220 (2000).
  • [31] M. Kotas, “Combined application of independent component analysis and projective filtering to fetal ECG extraction”, Biocybernetics and Biomedical Engineering 28 (1), 75-93 (2008).
  • [32] M. Jezewski, J. Wrobel, P. Labaj, J.M. Leski, N. Henzel, K. Horoba, and J. Jezewski, “Some practical remarks on neural networks approach to fetal cardiotocograms classification”, 29th Annual Int. Conf. IEEE Engineering in Medicine and Biology Society, Lyon 7, 5170-5173 (2007).
  • [33] P. Baxter, G. Spence, and J. McWhirter, “Blind signal separation on real data: tracking and implementation”, Proc. 6th Int. Symp. Independent Compon. Anal. Blind Signal Separ., Lecture Notes in Computer Science 3889, 327-334 (2006).
  • [34] M.J. Taylor, M.J. Smith, M. Thomas, A.R. Green, F. Cheng, S. Oseku-Afful, L.Y. Wee, N.M. Fisk, and H.M. Gardiner, “Non-invasive fetal electrocardiography in singleton and multiple pregnancies”, Int. J. Obstetrics and Gynaecology 110 (7), 668-678 (2003).
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-308ccb0d-c272-42d1-b15c-ef6d80307e2d
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