Ten serwis zostanie wyłączony 2025-02-11.
Nowa wersja platformy, zawierająca wyłącznie zasoby pełnotekstowe, jest już dostępna.
Przejdź na https://bibliotekanauki.pl

PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2017 | Vol. 37, no. 3 | 453--465
Tytuł artykułu

ECG signals reconstruction in subbands for noise suppression

Autorzy
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this study, we propose a combination of two methods for ECG noise suppression. The first one is the robust principal component analysis, applied to QRS complexes reconstruction. The second is the method of weighted averaging of nonlinearly aligned signal cycles. The novelty of the approach consists in the way these methods are combined. First, a processed ECG signal is decomposed into three spectral subbands, of high, medium and low frequency. Then both methods are applied in such a way that their operation is prevented from the most common unfavorable factors. RPCA reconstructs QRS complexes in a medium and high frequency subbands added. This makes the method more immune to low frequency artifacts that can be caused by electrodes motion. Dynamic time-warping is performed on the medium frequency subband which again prevents the procedure from the unfavorable influence of electrode motion artifacts. After the warping paths have been determined, the weighted addition of nonlinearly aligned signal cycles is executed, separately in the three subbands, with optimal weights estimated in each subband. Finally, by the appropriate addition of the obtained signals, the whole spectrum ECG is reconstructed. In the experimental section, the method was investigated with the use of real and artificially generated signals. In both cases, it allowed for effective suppression of noise, preserving important features of the processed signals. When it was applied to ECG enhancement prior to determination of the QT interval, the measurements appeared to be remarkably immune to different types of noise.
Wydawca

Rocznik
Strony
453--465
Opis fizyczny
Bibliogr. 34 poz., rys., tab., wykr.
Twórcy
autor
autor
Bibliografia
  • [1] van Alsté JA, van Eck W, Herrmann OE. ECG baseline wander reduction using linear phase filters. Comput Biomed Res 1986;19:417–27.
  • [2] Antzelevitch C. Role of spatial dispersion of repolarization in inherited and acquired sudden cardiac death syndromes. Am J Physiol Heart Circ Physiol 2007;293(4):2024–38.
  • [3] Augustyniak P. Time-frequency modelling and discrimination of noise in the electrocardiogram. Physiol Meas 2003;24:753–67.
  • [4] Augustyniak P. Optimal coding of vectorcardiographic sequences using spatial prediction. IEEE Trans Inf Technol Biomed 2007;11(3):305–11.
  • [5] Bellman R, Dreyfus S. Applied dynamic programming. New Jersey: Princeton Univ. Press; 1962.
  • [6] Berger RD, Kasper EK, Baughman KL, Marban E, Calkins H, Tomaselli GF. Beat-to-beat QT interval variability novel evidence for repolarization liability in ischemic and nonischemic Kohn87 dilated cardiomyopathy. Circulation 1997;96:1557–65.
  • [7] Croux C, Ruiz-Gazen A. A fast algorithm for robust principal components based on projection pursuit. Compstat: Proceedings in Computational Statistics; 1996. p. 211–7.
  • [8] Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 2000;101(23):E215–20.
  • [9] Gupta L, Molfese DL, Tammana R, Simos PG. Nonlinear alignment and averaging for estimating the evoked potential. IEEE Trans Biomed Eng 1996;43(4):348–56.
  • [10] Hamilton PS, Tompkins WJ. Quantitative investigation of QRS detection rules using the MIT/BIH arrhythmia database. IEEE Trans Biomed Eng 1986;33:1157–65.
  • [11] Hossjer O, Croux C. Generalizing univariate signed rank statistics for testing and estimating a multivariate location parameter. Nonparamet Stat 1995;4:293–308.
  • [12] Hu X, Nenov V. A single-lead ECG enhancement algorithm using a regularized data-driven filter. IEEE Trans Biomed Eng 2006;53:347–51.
  • [13] Jenkala W, Latifa R, Toumanaria A, Dlioua A, El B'charria O, Maoulainine FMR. An efficient algorithm of ECG signal denoising using the adaptive dual threshold filter and the discrete wavelet transform. Biocybern Biomed Eng 2016;36:499–508.
  • [14] Kohn AF. Phase distortion in biological signal analysis caused by linear phase FIR filters. Med Biol Eng Comput 1987;25:231–8.
  • [15] Kors JA, van Herpen G. Measurement error as a source of QT dispersion: a computerised analysis. Heart 1998;80:453–8.
  • [16] Kotas M. Projective filtering of time-aligned ECG beats for repolarization duration measurement. Comput Methods Progr Biomed 2007;85(2):115–23.
  • [17] Kotas M. Projective filtering of time-warped ECG beats. Comput Biol Med 2008;38:127–37.
  • [18] Kotas M. Robust projective filtering of time-warped ECG beats. Comput Methods Progr Biomed 2008;92(2):161–72.
  • [19] Kotas M. Combined application of independent component analysis and projective filtering to fetal ECG extraction. Biocybern Biomed Eng 2008;28(1):75–93.
  • [20] Kotas M, Pander T, Leski J. Averaging of nonlinearly aligned signal cycles for noise suppression. Biomed Signal Process Control 2015;21:157–68.
  • [21] Laguna P, Mark RG, Goldberg A, Moody GB. A database for evaluation of algorithms for measurement of QT and other waveform intervals in the ECG. Comput Cardiol 1997;24:673–6.
  • [22] Laguna P, Thakor NV, Caminal P, Jane R, Yoon H. New algorithm for QT interval analysis in 24 h Holter ECG: performance and applications. Med Biol Eng Comput 1990;28:67–73.
  • [23] Leski JM. Robust weighted averaging. IEEE Trans Biomed Eng 2002;49(8):796–804.
  • [24] Malik M, Batchvarov V. Measurement, interpretation and clinical potential of QT dispersion. J Am Coll Cardiol 2000;36:1749–66.
  • [25] McSharry PE, Clifford GD, Tarassenko L, Smith LA. A dynamical model for generating synthetic electrocardiogram signals. IEEE Trans Biomed Eng 2003;50.3:289–94.
  • [26] Momot A. Methods of weighted averaging of ECG signals using Bayesian inference and criterion function minimization. Biomed Signal Process Control 2009;4(2):162–9.
  • [27] Moroñ T. Averaging of time-warped ECG signals for QT interval measurement, information technologies in medicine. Springer International Publishing; 2016. p. 291–302.
  • [28] Pander T. A new approach to robust, weighted signal averaging. Biocybern Biomed Eng 2015;35:317–27.
  • [29] Petitjean F, Ketterlin A, Gancarski P. A global averaging method for dynamic time warping, with applications to clustering. Pattern Recognit 2011;44:678–93.
  • [30] Poornachandra S. Wavelet-based denoising using subband dependent threshold for ECG signals. Digit Signal Process 2008;18:49–55.
  • [31] Rousseeuw PJ, Croux C. Alternatives to the median absolute deviation. J Am Stat Assess 1993;88:1273–83.
  • [32] Sharma LN, Dandapat S, Mahanta A. ECG signal denoising using higher order statistics in wavelet subbands. Biomed Signal Process Control 2010;5(3):214–22.
  • [33] Schreiber T, Kaplan D. Nonlinear noise reduction for electrocardiograms. Chaos 1996;6:87–92.
  • [34] Tikkanen PE, Sellin LC, Kinnunen HO, Huikuri HV. Using simulated noise to define optimal QT intervals for computer analysis of ambulatory ECG. Med Eng Phys 1999;21:15–25.
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-057efbfc-29b2-4811-a79c-b8b1b5b22a25
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.