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Analysis of lung auscultatory phenomena using the Wigner-Ville Distribution

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Języki publikacji
EN
Abstrakty
EN
In this paper the authors will try to discuss the applicability of Wigner-Ville Distribution for the digital analysis of auscultatory sounds. First of all, thte issues related to computer aided diagnosis are presented. Next, the methodology of research is shown and subsequently, the types of sounds are described. Another element of this work is the presentation of issues related to the digital signal processing including the Short-Time Fourier Transform(STFT), Wigner-Ville Distribution (WVD), and its variation - Smoothed Wigner-Ville Distribution (SWVD). This paper summarizes the results obtained using STFT and SWVD, showing SWVD more useful to detect the type of auscultatory sounds.
Rocznik
Strony
7--16
Opis fizyczny
Bibliogr. 11 poz., rys.
Twórcy
autor
  • Institute of Computer Science, Maria Curie-Skłodowska University, pl. M. Curie-Skłodowskiej 1, 20-036 Lublin, Poland
  • The State School of Higher Education in Chełm, Pocztowa 54, 22-100 Chełm, Poland
  • Institute of Computer Science, Maria Curie-Skłodowska University, pl. M. Curie-Skłodowskiej 1, 20-036 Lublin, Poland
  • Department of Biomechanics and Computer Science, External Faculty of Physical Education in Biała Podlaska Akademicka 2, 21-500 Biała Podlaska, Poland
  • Clinical Nursing Department, Medical University of Warsaw, Ciołka 27, 01-445 Warsaw, Poland
autor
  • Institute of Precision and Biomedical Engineering, Warsaw University of Technology, Św. Andrzeja Boboli 8, 02-525 Warsaw, Poland
Bibliografia
  • 1] Goswami J. C., Chan A. K., Fundamentals of Wavelets Theory, Algorithms and Application, Wiley (2011).
  • [2] Zieliński T. P., Digital signal processing : from theory to applications, in Polish Cyfrowe Przetwarzanie Sygnałów - Od teorii do zastosowań, Warszawa: WKŁ (2009).
  • [3] Claasen T. A. C. M., Mecklenbrauker W. F. G., The Wigner - Ville Distribution — A Tool for Time - Frequency Signal Analysis. Part I: Continuous Time Signals, Philips J. Res. 35 (1980): 217.
  • [4] Szczeklik A., Choroby wewnętrzne - Stan wiedzy na rok 2010, Kraków: Medycyna Praktyczna (2010).
  • [5] Andre Q., Time-Frequency Analysis, Digital Signal Processing Using Matlab, Wiley (2008): 279.
  • [6] Maciuk M., Kuniszyk–Jóźkowiak W., Kuc K., Analysis of respiratory sounds, in Polish: Analiza Fenomenów Osłuchowych, Scientific Bulletin of Chełm, Section of Techical Sciences, PWSZ Chełm 1 (2008).
  • [7] Pasterkamp H., Kraman S. S., Wodicka G. R., Respiratory Sounds - Advanced Beyond Stethoscope, American Journal of Respiratory and Critical Care Medicine 156 (1997): 974.
  • [8] Dokur Z., Olmez T., Clasification of Respiratory Sounds by Using an Artificial Neural Network, International Journal of Pattern Recognition (2003): 567.
  • [9] Vanuccini L., Rossi M., Pasquali G., A new method to detect cracles in respiratory sounds, Technology and Health Care 6 (1998): 75.
  • [10] Kandaswamy A., Kumar C. S., Ramanathan R. P., Jayaraman S., Malmurugan N., Neural classification of lung sounds using wavelet coefficients, Computers in Biology and Medicine 34 (2004): 523.
  • [11] Ville J., Theorie et applications de la notion de signal analitique, Cables et Transmissions 2A (1) (1948): 61.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d48306e6-653f-4376-927b-c31bc14bfe10
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