Different Scenarios on Denoising of Signals in the Intrinsic Mode Function Selection Framework

References

• N. E. Huang, Z. Shen, S. R. Long, M. L. C. Wu, H. H. Shih, Q. N. Zheng, N. C. Yen, C. C. Tung, and H. H. Liu, “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proc. R. Soc. London A Math. Phys. Sci., Vol. 454, no. 1971, pp. 903–95, Mar. 1998.
   Web of Science ®Google Scholar
• Z. Wu, and N. E. Huang, “A study of the characteristics of white noise using the empirical mode decomposition method,” Proc. R. Soc. London A Math. Phys. Sci., Vol. 460, no. 2046, pp. 1597–611, Jun. 2004.
   Web of Science ®Google Scholar
• P. Flandrin, G. Rilling, and P. Gonçalvés, “Empirical mode decomposition as a filter bank,” IEEE Signal Proc. Lett., Vol. 11, no. 2, pp. 112–4, Feb. 2004.
   Web of Science ®Google Scholar
• G. Rilling, P. Flandrin, and P. Gonçalvés, “Empirical mode decomposition, fractional Gaussian noise and hurst exponent estimation,” in Proceeding of the IEEE International Conference on Acoustic, Speech, and Signal Processing, Philadelphia, PA, 2005, pp. 489–92.
   Google Scholar
• A.-O. Boudraa, and J.-C. Cexus, “EMD-based signal filtering,” IEEE Trans. Instr. Meas., Vol. 56, no. 6, pp. 2196–202, Dec. 2007.
   Web of Science ®Google Scholar
• A. Komaty, A.-O. Boudraa, B. Augier, and D. Daré-Emtivaz, “EMD-based filtering using similarity measure between probability density functions of IMFs,” IEEE Trans. Instr. Meas., Vol. 63, no. 1, pp. 27–34, Jan. 2014.
   Web of Science ®Google Scholar
• T.-Y. Sun, C.-C. Liu, J.-H. Jheng, T.-Y. Tsai, “An efficient noise reduction algorithm using empirical mode decomposition and correlation measurement,” in Proceeding of the IEEE International Symposium on Intelligent Signal Processing and Communication System, Bangkok, 2008, pp. 286–89.
   Google Scholar
• S. Wu, Z. Liu, and B. Liu, “Enhancement of lidar backscatters signal-to-noise ratio using empirical mode decomposition method,” Opt. Commun., Vol. 267, no. 1, pp. 137–44, Nov. 2006.
   Web of Science ®Google Scholar
• N. Chatlani, and J. J. Soraghan, “EMD-based filtering (EMDF) of low-frequency noise for speech enhancement,” IEEE Trans. Audio Speech Lang. Process., Vol. 20, no. 4, pp. 1158–66, May 2012.
   Google Scholar
• P. Flandrin, P. Gonçalvés, and G. Rilling, “Detrending and denoising with empirical mode decompositions,” in Proceeding of the 12th European Signal Processing Conference, Vienna, 2004, pp. 1581–4.
   Google Scholar
• D. Baptista de Souza, J. Chanussot, and A.-C. Favre, “On selecting relevant intrinsic mode functions in empirical mode decomposition: An energy-based approach,” in Proceeding of the IEEE International Conference on Acoustic, Speech and Signal Processing, Florence, 2014, pp. 325–9.
   Google Scholar
• A. Mert, and A. Akan, “Detrended fluctuation thresholding for empirical mode decomposition based denoising,” Digit. Signal Process., Vol. 32, no. 1, pp. 48–56, Jun. 2014.
   Web of Science ®Google Scholar
• Z. K. Peng, P. W. Tse, and F. L. Chu, “A comparison study of improved Hilbert-Huang transform and wavelet transform: Application to fault diagnosis for rolling bearing,” Mech. Syst. Signal Process., Vol. 19, no. 5, pp. 974–88, Sep. 2005.
   Web of Science ®Google Scholar
• L. Xiaofeng, and L. Mingjie, “The de-noising method of EMD threshold based on correlation,” in Proceeding of the IEEE 10th International Conference on Signal Processing, Beijing, 2010, pp. 2613–6.
   Google Scholar
• A. Mert, and A. Akan, "EEG denoising based on empirical mode decomposition and mutual information," in Proceeding of the IFMBE XIII Mediterranean Conference on Medical Biological Engineering and Computing, Seville, 2013, pp. 631–4.
   Google Scholar
• C.-Y. Tseng, and H. Lee, “Entropic interpretation of empirical mode decomposition and its applications in signal processing,” Adv. Adapt. Data Anal., Vol. 2, no. 4, pp. 429–49, Oct. 2010.
   Google Scholar
• K. Khaldi, M. Turki–Hadj Alouane, and A.-O. Boudraa, “A new EMD denoising approach dedicated to voiced speech signals,” in Proceeding of the IEEE 2nd International Conference on Signals Circuits System, Nabeul, 2008, pp. 113–7.
   Google Scholar
• A. Chacko, and S. Ari, “Denoising of ECG signals using empirical mode decomposition based technique,” in Proceeding of the IEEE International Conference on Advances in Engineering, Science and Management, Nagapattinam, 2012, pp. 6–9.
   Google Scholar
• M. Blanco-Velasco, B. Weng, and K. E. Barner, “ECG signal denoising and baseline wander correction based on the empirical mode decomposition,” Comput. Biol. Med., Vol. 38, no. 1, pp. 1–13, Jan. 2008.
   PubMed Web of Science ®Google Scholar
• Y. Liu, Y. Li, H. Lin, and H. Ma, “An amplitude-preserved time-frequency peak filtering based on empirical mode decomposition for seismic random noise reduction,” IEEE Trans. Geosci. Remote Sens. Lett., Vol. 11, no.5, pp. 896–900, May 2014.
   Web of Science ®Google Scholar
• M. Suchetha, and N. Kumaravel, “Empirical mode decomposition-based subtraction techniques for 50 Hz interference reduction from electrocardiogram,” IETE J. Res., Vol. 59, no. 1, pp. 55–62, Jan.–Feb. 2013.
   Web of Science ®Google Scholar
• A. Ukte, A. Kizilkaya, and M. D. Elbi, “Two empirical methods for improving the performance of statistical multirate high-resolution signal reconstruction,” Digit. Signal Process., Vol. 26, no. 3, pp. 36–49, Mar. 2014.
   Web of Science ®Google Scholar
• G. Rilling, P. Flandrin, and P. Gonçalves, “On empirical mode decomposition and its algorithms,” in IEEE-EURASIP Workshop Nonlinear Signal Image Processing, Grado, 2003, pp. 444–7.
   Google Scholar
• A. Linderhed, “Variable sampling of the empirical mode decomposition of two-dimensional signals,” Int. J. Wavelets Multiresolut. Inf. Process., Vol. 3, no. 3, pp. 435–52, Sep. 2005.
   Web of Science ®Google Scholar
• Y. Kopsinis, and S. McLaughlin, “Development of EMD-based denoising methods inspired by wavelet thresholding,” IEEE Trans. Signal Process., Vol. 57, no. 4, pp. 1351–62, Apr. 2009.
   Web of Science ®Google Scholar
• W. S. Sarle, “Donoho-Johnstone benchmarks: Neural net results,” Available: ftp://ftp.sas.com/pub/neural/data/dojo_test.txt.
   Google Scholar
• E. Keogh, J. Lin, and A. Fu, “HOT SAX: Efficiently finding the most unusual time series subsequence: Algorithms and applications,” in Proceeding of the IEEE International Conference on Data Mining, Houston, TX, 2005, pp. 226–33. Available: http://www.cs.ucr.edu/∼eamonn/discords/ECG_data.zip, (chfdb_chf01_275.txt ).
   Google Scholar
• R. G. Andrzejak, K. Lehnertz, F. Mormann, C. Rieke, P. David, and C. E. Elger, “Indications of nonlinear deterministic and finite dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state,” Phys. Rev. E, Vol. 64, no. 6, pp. (061907) 1–8, Dec. 2001, Available: http://epileptologiebonn.de/cms/upload/workgroup/lehnertz/S.zip (S002.txt).
   PubMed Web of Science ®Google Scholar