Noise components identification in biomedical signals based on Empirical Mode Decomposition

Hilbert-Huang Transform (HHT) is composed of the Empirical Mode Decomposition (EMD) as the first step of the procedure and Hilbert Spectral analysis (HSA) as the second step. It is a recent tool in the analysis of signals originating from nonlinear processes as well as nonstationary signals. Empirical Mode Decomposition produces a set of Intrinsic Mode Functions and the core idea is based on the assumption that any data consists of different simple intrinsic modes of oscillations. Statistical significance of the Intrinsic Mode Functions and partial signal reconstruction are investigated in this paper. Application of Hilbert-Huang Transform on biomedical signals such as ECG from MIT-BIH database and experimental respiratory signals acquired by means of accelerometers, reveal the adaptive nature of the method.

[1]  Lihua Yang,et al.  The study of the intermittency test filtering character of Hilbert-Huang transform , 2005, Math. Comput. Simul..

[2]  N. Huang,et al.  A study of the characteristics of white noise using the empirical mode decomposition method , 2004, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[3]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

[4]  B. N. Krupa,et al.  The application of empirical mode decomposition for the enhancement of cardiotocograph signals , 2009, Physiological measurement.

[5]  Philip Constantinou,et al.  Comparative study of Empirical Mode Decomposition applied in experimental biosignals , 2008, 2008 8th IEEE International Conference on BioInformatics and BioEngineering.

[6]  Gabriel Rilling,et al.  Empirical mode decomposition as a filter bank , 2004, IEEE Signal Processing Letters.

[7]  A.J. Nimunkar,et al.  R-peak Detection and Signal Averaging for Simulated Stress ECG using EMD , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[8]  Ramón González-Camarena,et al.  Crackle sounds analysis by empirical mode decomposition. Nonlinear and nonstationary signal analysis for distinction of crackles in lung sounds. , 2007, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

[9]  李幼升,et al.  Ph , 1989 .

[10]  Manuel Blanco-Velasco,et al.  ECG signal denoising and baseline wander correction based on the empirical mode decomposition , 2008, Comput. Biol. Medicine.

[11]  M. S. Woolfson,et al.  Application of empirical mode decomposition to heart rate variability analysis , 2001, Medical and Biological Engineering and Computing.

[12]  R. Jane,et al.  Application of the Empirical Mode Decomposition method to the Analysis of Respiratory Mechanomyographic Signals , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[13]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.