PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Powiadomienia systemowe
  • Sesja wygasła!
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper, a new approach for robust, weighted averaging of time-aligned signals is proposed. Suppression of noise in such case can be achieved with the use of the averaging technique. The signals are time-aligned and then the average template is determined. To this end, the arithmetic mean operator is often applied to the synchronized signal samples or its various modifications. However, the disadvantage of the mean operator is its sensitivity to outliers. The weighted averaging operation can be regarded as special case of clustering. For that reason in this work the averaging process is formulated as the problem of certain criterion function minimization and a few different cost functions are employed. The maximum likelihood estimator of location based on the generalized Cauchy distribution is used as the cost function. Such approach allows to suppress various types of impulsive noise. The proposed methods performance is experimentally evaluated and compared to the reference methods using electrocardiographic signal in the presence of the impulsive noise and the real muscle noise as well as the case of noise power variations.
Twórcy
autor
  • Silesian University of Technology, Faculty of Automatic Control, Electronic and Computer Science, Institute of Electronics, 16 Akademicka St., 44-100 Gliwice, Poland
Bibliografia
  • [1] Hassan U, Anwar M. Reducing noise by repetition: introduction to signal averaging. Eur J Phys 2010;30:453–65.
  • [2] Tompkins WJ, editor. Biomedical digital signal processing: C-language examples and laboratory experiments for the IBM PC. Upper Saddle River, NJ, USA: Prentice-Hall, Inc.; 1993.
  • [3] Leonowicz Z, Karvanen J, Shishkin S. Trimmed estimators for robust averaging of event-related potentials. J Neurosci Meth 2005;142:17–26.
  • [4] Köhler BU, Hennig C, Orglmeister R. The principles of software QRS detection. IEEE Eng Med Biol Mag 2002;21: 42–57.
  • [5] Jané R, Rix H, Caminal P, Laguna P, Jane R. Alignment methods for averaging of high-resolution cardiac signals: a comparative study of performance. IEEE Trans Biomed Eng 1991;38:571–9.
  • [6] De Silva A, Sinclair N, Liley D. Limitations in rapid extraction of evoked potentials using parametric modeling. IEEE Trans Biomed Eng 2012;59:1462–71.
  • [7] Walsh P, Kane N, Butler S. The clinical role of evoked potentials. J Neurol Neurosurg Psychiatr 2005;76:16–22.
  • [8] Plourde G. Auditory evoked potentials. Best Pract Res Clin Anaesthesiol 2006;20:129–39.
  • [9] Łeski J. Robust weighted averaging. IEEE Trans Biomed Eng 2002;49:796–804.
  • [10] Syed Z, Guttag JV. Prototypical biological signals. Proc IEEE Int Conf on Acoustics, Speech and Signal Process. 2007. pp. 397–400.
  • [11] Yager RR. On mean type aggregation. IEEE Trans Syst Man Cybern B: Cybern 1996;26:209–21.
  • [12] Dave RN, Krishnapuram R. Robust clustering methods: a unified view. IEEE Trans Fuzzy Syst 1997;5(2):270–93.
  • [13] Hathaway RJ, Bezdek JC, Hu Y. Generalized fuzzy c-means clustering strategies using lp norm distances. IEEE Trans Fuzzy Syst 2000;8:576–82.
  • [14] Momot A. Methods of weighted averaging of ECG signals using Bayesian inference and criterion function minimization. Biomed Signal Process Control 2009;4: 162–9.
  • [15] Shao M, Nikias CL. Signal processing with fractional lower order moments: a-stable processes and their applications. Proc IEEE 1993;81:986–1009.
  • [16] Pander T. An application of myriad m-estimator for robust weighted averaging. Int Conf on Man-Mach Interac (ICMMI 2013), Adv in Intell Syst Comput. 2013. pp. 265–72.
  • [17] Pander T, Przybyła T, Czabański R. An application of the Lp-norm in robust weighted averaging of biomedical signals. JMIT 2013;22:71–8.
  • [18] Bousseljot R, Kreiseler D, Schnabel A. Nutzung der EKG-Signaldatenbank CARDIODAT der PTB ber das Internet.. Biomedizinische Technik Band 40 Ergnzungsband 1995;1: S317.
  • [19] Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 2000;101(23): e215–20.
  • [20] Moody GB, Muldrow WE, Mark RG. A noise stress test for arrhythmia detectors. Comput Cardiol 1984;11:381–4.
  • [21] Huber PJ. Robust statistics. New York: Wiley; 1981.
  • [22] Lee Y, Kassam S. Generalized median filtering and related nonlinear filtering techniques. IEEE Trans Acoust Speech Signal Process 1985;33:672–83.
  • [23] Yin L, Yang R, Gabbouj M, Neuvo Y. Weighted median filters: a tutorial. IEEE Trans Circuits Syst II: Analog Digit Signal Process 1996;43:157–92.
  • [24] Rider P. Generalized Cauchy distributions. Ann Inst Stat Math 1957;9:215–23.
  • [25] Aysal T, Barner K. Meridian filtering for robust signal processing. IEEE Trans Signal Process 2007;55:3949–62.
  • [26] Kalluri S, Arce G. Adaptive weighted myriad filter algorithms for robust signal processing in alpha-stable noise environments. IEEE Trans Signal Process 1998;46: 322–34.
  • [27] Carrillo R, Aysal T, Barner K. A generalized Cauchy distribution framework for problems requiring robust behavior. EURASIP J Adv Signal Process 2010. http://dx.doi.org/10.1155/2010/312989.
  • [28] Clifford G. http://www.robots.ox.ac.uk/~gari/CODE/ECGtools/.
  • [29] Narasimhan SV, Narayana DD. Application of LMS adaptive predictive filtering for muscle artifact (noise) cancellation from EEG signals. Comput Electr Eng 1996;22:13–30.
  • [30] Pander T. Application of weighted myriad filters to suppress impulsive noise in biomedical signals. TASK Q 2004;8:199–216.
  • [31] McCulloch JH. Simple consistent estimators of stable distribution parameters. Commun Stat Simul 1986;15:1109–36.
  • [32] Gonzalez J, Paredes J, Arce G. Zero-order statistics: a mathematical framework for the processing and characterization of very impulsive signals. IEEE Trans Signal Process 2006;54:3839–51.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-1a83b5fa-0623-4fed-8fa3-19556db2e375
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.