Warianty tytułu
Języki publikacji
Abstrakty
In this paper, the investigation on effectiveness of the empirical mode decomposition (EMD) with non-local mean (NLM) technique by using the value of differential standard deviation for denoising of ECG signal is performed. Differential standard deviation is calculated for collecting information related to the input noise so that appropriate formation in EMD and NLM framework can be performed. EMD framework in the proposed methodology is used for reduction of the noise from the ECG signal. The output of the EMD passes through NLM framework for preservation of the edges and cancel the noise present in the ECG signal after the EMD process. The performance of the proposed methodology has been validated by using added white and color Gaussian noise to the clean ECG signal from MIT-BIH arrhythmia database at different signal to noise ratio (SNR). The proposed denoising technique shows lesser mean of percent root mean square difference (PRD), mean square error (MSE), and better mean SNR improvement compared to other well-known methods at different input SNR. The proposed methodology also shows lesser standard deviation PRD, MSE, and SNR improvement compared to other well-known methods at different input SNR.
Czasopismo
Rocznik
Tom
Strony
297--312
Opis fizyczny
Bibliogr. 27 poz., rys., tab., wykr.
Twórcy
autor
- Department of Electrical Engineering, NIT Rourkela, India, shailesh.aeie@gmail.com
autor
- Department of Electrical Engineering, NIT Rourkela, India, damo.3093@gmail.com
autor
- Department of Electrical Engineering, NIT Rourkela, India, pksahu@nitrkl.ac.in
Bibliografia
- [1] Rakshit M, Panigrahy D, Sahu PK. An improved method for R-peak detection by using Shannon energy envelope. Sadhana – Acad Proc Eng Sci Springer 2016;41:469–77. http://dx.doi.org/10.1007/s12046-016-0485-8.
- [2] Izzetoglu M, Devaraj A, Bunce S, Onaral B. Motion artifact cancellation in NIR spectroscopy using Wiener filtering. IEEE Trans Biomed Eng 2005;52:934–8. http://dx.doi.org/10.1109/TBME.2005.845243.
- [3] Lu G, Brittain J-S, Holland P, Yianni J, Green AL, Stein JF, et al. Removing ECG noise from surface EMG signals using adaptive filtering. Neurosci Lett 2009;462:14–9. http://dx.doi.org/10.1016/j.neulet.2009.06.063.
- [4] Marque C, Bisch C, Dantas R, Elayoubi S, Brosse V, Pérot C. Adaptive filtering for ECG rejection from surface EMG recordings. J Electromyogr Kinesiol 2005;15:310–5. http://dx.doi.org/10.1016/j.jelekin.2004.10.001.
- [5] Joshi SL, Vatti RA, Tornekar RV. A survey on ECG signal denoising techniques. 2013 Int Conf Commun Syst Netw Technol. 2013. pp. 60–4. http://dx.doi.org/10.1109/CSNT.2013.22.
- [6] Singh BN, Tiwari AK. Optimal selection of wavelet basis function applied to ECG signal denoising. Digit Signal Process A Rev J 2006;16:275–87. http://dx.doi.org/10.1016/j.dsp.2005.12.003.
- [7] Awal MA, Mostafa SS, Ahmad M, Rashid MA. An adaptive level dependent wavelet thresholding for ECG denoising. Biocybern Biomed Eng 2014;34:238–49. http://dx.doi.org/10.1016/j.bbe.2014.03.002.
- [8] Jenkal W, Latif R, Toumanari A, Dliou A, El B'Charri 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. http://dx.doi.org/10.1016/j.bbe.2016.04.001.
- [9] Gokgoz E, Subasi A. Effect of multiscale PCA de-noising on EMG signal classification for diagnosis of neuromuscular disorders. J Med Syst 2014;38. http://dx.doi.org/10.1007/s10916-014-0031-3.
- [10] Güler I, Übeyli ED. ECG beat classifier designed by combined neural network model. Pattern Recognit 2005;38:199–208. http://dx.doi.org/10.1016/j.patcog.2004.06.009.
- [11] Sameni R, Shamsollahi MB, Jutten C, Clifford GD. A nonlinear Bayesian filtering framework for ECG denoising. IEEE Trans Biomed Eng 2007;54:2172–85. http://dx.doi.org/10.1109/TBME.2007.897817.
- [12] Kabir MA, Shahnaz C. Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains. Biomed Signal Process Control 2012;7:481–9. http://dx.doi.org/10.1016/j.bspc.2011.11.003.
- [13] Blanco-Velasco M, Weng B, Barner KE. ECG signal denoising and baseline wander correction based on the empirical mode decomposition. Comput Biol Med 2008;38:1–13. http://dx.doi.org/10.1016/j.compbiomed.2007.06.003.
- [14] Tracey BH, Miller EL. Nonlocal means denoising of ECG signals. IEEE Trans Biomed Eng 2012;59:2383–6. http://dx.doi.org/10.1109/TBME.2012.2208964.
- [15] Cuomo S, De Pietro G, Farina R, Galletti A, Sannino G. A novel O(n) numerical scheme for ECG signal denoising. Proc Comput Sci 2015;51:775–84. http://dx.doi.org/10.1016/j.procs.2015.05.198.
- [16] Cuomo S, De Michele P, Galletti A, Marcellino L. A GPU-parallel algorithm for ECG signal denoising based on the NLM method. Proc. – IEEE 30th Int. Conf. Adv. Inf. Netw. Appl. Work. WAINA. 2016. pp. 35–9. http://dx.doi.org/10.1109/WAINA.2016.110.
- [17] Cuomo S, De Pietro G, Farina R, Galletti A, Sannino G. A revised scheme for real time ECG Signal denoising based on recursive filtering. Biomed Signal Process Control 2016;27:134–44. http://dx.doi.org/10.1016/j.bspc.2016.02.007.
- [18] Sivaraman J, Uma G, Venkatesan S, Umapathy M, Kumar NK. A study on atrial TA wave morphology in healthy subjects: an approach using P wave signal-averaging method. J Med Imaging Heal Inform 2014;4:675–80. http://dx.doi.org/10.1166/jmihi.2014.1306.
- [19] Rakshit M, Panigrahy D, Sahu PK. EKF with PSO technique for delineation of P and T wave in electrocardiogram (ECG) signal. 2015 2nd Int. Conf. Signal Process. Integr. Networks. IEEE; 2015. p. 696–701. http://dx.doi.org/10.1109/SPIN.2015.7095293.
- [20] Panigrahy D, Sahu PK. Extraction of fetal electrocardiogram (ECG) by extended state Kalman filtering and adaptive neuro-fuzzy inference system (ANFIS) based on single channel abdominal. Sadhana – Acad Proc Eng Sci Springer 2015;40:1091–104.
- [21] Panigrahy D, Rakshit M, Sahu PK. An efficient method for fetal ECG extraction from single channel abdominal ECG. 2015 Int. Conf. Ind. Instrum. Control. ICIC. 2015. pp. 1083–8. http://dx.doi.org/10.1109/IIC.2015.7150908.
- [22] Panigrahy D, Sahu PK. Extended Kalman smoother with differential evolution technique for denoising of ECG signal. Australas Phys Eng Sci Med 2016;39:783–95. http://dx.doi.org/10.1007/s13246-016-0468-4.
- [23] Panigrahy D, Sahu PK. Extraction of fetal ECG signal by an improved method using extended Kalman smoother framework from single channel abdominal ECG signal. Australas Phys Eng Sci Med 2017;40. http://dx.doi.org/10.1007/s13246-017-0527-5.
- [24] Flandrin P, Rilling G, Goncalves P. Empirical mode decomposition as a filter bank. IEEE Signal Process Lett 2004;11:112–4. http://dx.doi.org/10.1109/LSP.2003.821662.
- [25] Zhang Y, Zhao J, Zhang W, Fan Q. Digital hologram apodization using Tukey-window function. Acta Photonica Sin 2007;36.
- [26] Chen C, Shi J, Huang P. Unsupervised discriminant analysis based on the local and non-local mean. Phys Proc 2012;24:1967–73. http://dx.doi.org/10.1016/j.phpro.2012.02.289.
- [27] Goldberger AL, Amaral LAN, 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;101:215–20. http://dx.doi.org/10.1161/01.CIR.101.23.e215.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-6475c0b6-d53d-465e-a28e-fd5508a5e6c4