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Performance comparison of wavelet thresholding techniques on weak ECG signal denoising

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Warianty tytułu
PL
Porównanie działania metod progowania falkowego w odszumianiu sygnałów EKG
Języki publikacji
EN
Abstrakty
EN
The Electrocardiogram (ECG) signal is a biological non-stationary signal which contains important information about rhythms of heart. ECG signals can be buried by various types of noise. These types can be electrode movement, strong electromagnetic effect and muscle noise. Noisy ECG signal has been denoised using signal processing. This paper presents a weak ECG signal denoising method based on intervaldependent thresholds of wavelet analysis. Several experiments were conducted to show the effectiveness of the interval-dependent thresholding method and compared the results with the soft and hard wavelet thresholding methods for denoising. The results are evaluated by calculating the root mean square error and the correlation coefficient.
PL
W artykule przedstawiono metodę odszumiania sygnałów elektrokardiografu w oparciu o analizę falkową. W rozwiązaniu zastosowano progowanie przedziałowo-zależne. Na podstawie poczynionych eksperymentów oraz wyznaczonych wartości RMS błędu i współczynnika korelacji wykazano jego skuteczność. Dodatkowo dokonano porównania otrzymanych wyników z działaniem metod miękkiego i twardego progowania falkowego.
Rocznik
Strony
63--66
Opis fizyczny
Bibliogr. 27 poz., rys., tab.
Twórcy
  • Firat University, Technical Education Faculty, Department of Electronics and Computer Science, 23119, Elazig
autor
  • Firat University, Technology Faculty, Department of Electrics and Electronics Engineering, 23119, Elazig
autor
  • Firat University, Technology Faculty, Department of Electrics and Electronics Engineering, 23119, Elazig
autor
  • Firat University, Engineering Faculty, Department of Electrics-Electronics Engineering, 23119, Elazig
Bibliografia
  • [1] Borries R.V., Pierluissi J. H., and Nazeran H., Redundant Discrete Wavelet Transform for ECG Signal Processing, Biomedical Soft Computing and Human Sciences, (2009), Vol.14,No.2,pp.69-80
  • [2] Üstündağ M., Gökbulut M., Şengür A., Ata F., Denoising of weak ECG signals by using wavelet analysis and fuzzy thresholding, Network Modelling Analysis in Health Informatics and Bioinformatics, (2012), pp.135-140
  • [3] Yue Li., Yang B., Introduction of chaotic oscillator detection, Beijing: Publish House of Electronics Industry, (2004)
  • [4] Sayadi O., Shamsollahi M.B., ECG denoising and compression using a modified extended kalman filter structure, IEEE Trans. on Biomedical Engineering, (2008) vol.55, no.9, pp. 2240-2248
  • [5] Lu G., Brittain J.S., Holland P., Yianni J., Green A.L., Stein J.F., Aziz T.Z., Wang S., Removing ECG noise from surface EMG signals using adaptive filtering, Neuroscience Letters, (2009), vol.462, no.1, pp. 14-19
  • [6] Sivannarayayana N., Reddy D.C., Biorthogonal wavelet transforms for ECG parameters estimation, (1999), Medical Engineering and Physics, vol.21, pp.167-174
  • [7] Zhidong Z., Min P., ECG denoising by sparse wavelet shrinkage, in Bioinformatics and Biomedical Engineering, (2007), pp.786-789
  • [8] Alfaouri M., Daqrouq K., ECG denoising by sparse wavelet shrinkage, American Journal of Applied Sciences, (2008), vol.5(3), pp.276-281
  • [9] Sayadi O., Shamsollahi M.B., ECG denoising with adaptive bionic wavelet transform, in 28th Annual International Conf. of IEEE on Eng. In Med. and Biology Society EMBS’06, (2006), pp.6597-6600
  • [10] Beheshti S., Nikvand N., Fernando X.N., Soft thresholding by noise invalidation, in Communications 24th Biennial Symposium on, (2008), pp.235-238
  • [11] Donoho D., Johnstone I., Ideal spatial adaptation by wavelet shrinkage, Biometrika, (1994), vol.81, pp.425-455
  • [12] Karel J.M.H., Peeters R.L.M., Westra R.L., Moermans K.M.S., Haddad S.A.P., Serdijn W.A., Optimal discrete wavelet design for cardiac signal processing, Proceedings of the IEEE, Engineering in Medicine and Biology 27th Annual Conference, (2005)
  • [13] Mahamoodabadi S.Z., Ahmedian A., Abolhasani M.D., ECG feature extraction using daubechies wavelet, Proceedings of the fifth IASTED International Conference Visualization, Imaging and Image Processing, (2005)
  • [14] Kumari R.S.S., Bharathi S., Sadasivam V., Design of optimal discrete wavelet for ECG signal using orthogonal filter bank, International Conference on Computational Intelligence and Multimedia Applications, IEEE, (2007)
  • [15] Nikolaev N., Nikolov Z., Gotchev A., Egiazarian K., Wavelet domain Wiener Filtering for ECG denoising using improved signal estimate, IEEE, (2000), pp.3578-3581
  • [16] Zhang Q., Rossel R.A., Choi P., Denoising of gamma-ray signals by interval-dependent thresholds of wavelet analysis, Measurement Science and Technology, (2006), pp.731-735
  • [17] Mallat, S.G., A theory for multiresolution signal decomposition: The Wavelet representation, IEEE Transaction on Pattern Analysis and Machine Intelligence, (1989), pp.674-693
  • [18] Hossain M.S., Amin N., ECG Compression using subband thresholding of the wavelet coefficients, Australian Journal of Basic and Applied Sciences, (2011), pp.739-749
  • [19] Arı N., Özen Ş., Çolak Ö.H., Dalgacık teorisi, Palme Yayıncılık, (2008)
  • [20] Fletcher A.K., Denoising via Recursive Wavelet Thresholding, Master of Science in Electrical Engineering in the Graduate Division of the University of California, Berkele, (2002)
  • [21] Kumar P.S., Arumuganathan R., Sivakumar K., Vimal C., Removal of Ocular artifacts in the EEG through Wavelet Transform without using an EOG Reference Channel, Int.J.Open Problems Compt. Math., (2008), vol.1, no.3, pp.188- 200
  • [22] Bartušek K., Přinosil J., Smékal Z., Wavelet-based de-noising techniques in MRI, Computer Methods and Programs in Biomedicine, (2011), vol. 104, Issue 3, Pages 480-488
  • [23] Aly O.A.M., Omar A.S., Elsherbeni A.Z., Detection and localization of RF radar pulses in noise environments using wavelet packet transform and higher order statistics, Progress in Electromagnetics Research, (2006), pp.301-317
  • [24] Internet: Harvard-MIT Division of Health Sciences and Technology, http://ecg.mit.edu/.
  • [25] Aggarwal R., Singh J.K., Gupta V.K., Rathore S., Tiwari M., Khare A., Noise reduction of speech signal using wavelet transform with modified universal threshold, International Journal of Comp. App., (2011), Vol.20, No.5, pp.14-19
  • [26] Internet: Correlation Coefficients, http://www.jerrydallal.com.
  • [27] Internet: Root Mean Square Error, http://www.mathinteractive.com/.
Typ dokumentu
Bibliografia
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