{"title":"Electrocardiogram Signal Denoising Using a Hybrid Technique","authors":"R. Latif, W. Jenkal, A. Toumanari, A. Hatim","volume":123,"journal":"International Journal of Bioengineering and Life Sciences","pagesStart":256,"pagesEnd":260,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/10006721","abstract":"This paper presents an efficient method of electrocardiogram signal denoising based on a hybrid approach. Two techniques are brought together to create an efficient denoising process. The first is an Adaptive Dual Threshold Filter (ADTF) and the second is the Discrete Wavelet Transform (DWT). The presented approach is based on three steps of denoising, the DWT decomposition, the ADTF step and the highest peaks correction step. This paper presents some application of the approach on some electrocardiogram signals of the MIT-BIH database. The results of these applications are promising compared to other recently published techniques.","references":"[1]\tChaudhary, M.S., Kapoor, R.K., Sharma, A.K., (2014) Comparison between different wavelet transforms and thresholding techniques for ECG denoising. In: International Conference on Advances in Engineering and Technology Research (ICAETR), IEEE, pp. 1-6. \r\n[2]\tTracey, B.H., Miller, E.L., (2012) Nonlocal means denoising of ECG signals. IEEE Transactions on Biomedical Engineering, 59(9), pp. 2383-2386. \r\n[3]\tCao, X., Li, Z., (2010) Denoising of ECG signal based on a comprehensive framework. In: International Conference on Multimedia Technology (ICMT), IEEE, pp. 1-4. October. \r\n[4]\tSameni, R., Shamsollahi, M. B., Jutten, C., Clifford, G. D., (2007) A nonlinear Bayesian filtering framework for ECG denoising. IEEE Transactions on Biomedical Engineering, 54(12), pp. 2172-2185. \r\n[5]\tKirst, M., Glauner, B., Ottenbacher, J., (2011) Using DWT for ECG motion artifact reduction with noise-correlating signals. In: Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE, pp. 4804-4807 August.\r\n[6]\tLu, Y., Yan, J., Yam, Y., (2009) Model-based ECG denoising using empirical mode decomposition. In: IEEE International Conference on Bioinformatics and Biomedicine. pp. 191-196. November.\r\n[7]\tNanavati, S.P., Panigrahi, P.K., (2005) Wavelets: Applications to image compression-I. Resonance, 10(2), pp. 52-61. \r\n[8]\tBanerjee, S., Gupta, R., Mitra, M., (2012) Delineation of ECG characteristic features using multiresolution wavelet analysis method. Measurement, 45(3), pp. 474\u201387.\r\n[9]\tJenkal, W., Latif, R., Toumanari, A., Dliou, A., El B'charri, O., (2015) An efficient method of ECG signals denoising based on an adaptive algorithm using mean filter and an adaptive dual threshold filter. Int Rev Comput Softw, 10(11).\r\n[10]\tGupta, V., Chaurasia, V., Shandilya, M., (2015) Random-valued impulse noise removal using adaptive dual threshold median filter. J Vis Commun Image Represent, 26, pp. 296\u2013304.\r\n[11]\tAwal, MA., Mostafa, SS., Ahmad, M., Rashid, MA., (2014) An adaptive level dependent wavelet thresholding for ECG denoising. Biocybern Biomed Eng, 34(4), pp. 238\u201349.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 123, 2017"}