Noise-Assisted Data Processing With Empirical Mode Decomposition in Biomedical Signals

In this paper, a methodology is described in order to investigate the performance of empirical mode decomposition (EMD) in biomedical signals, and especially in the case of electrocardiogram (ECG). Synthetic ECG signals corrupted with white Gaussian noise are employed and time series of various lengths are processed with EMD in order to extract the intrinsic mode functions (IMFs). A statistical significance test is implemented for the identification of IMFs with high-level noise components and their exclusion from denoising procedures. Simulation campaign results reveal that a decrease of processing time is accomplished with the introduction of preprocessing stage, prior to the application of EMD in biomedical time series. Furthermore, the variation in the number of IMFs according to the type of the preprocessing stage is studied as a function of SNR and time-series length. The application of the methodology in MIT-BIH ECG records is also presented in order to verify the findings in real ECG signals.

[1]  P. Constantinou,et al.  Experimental respiratory signal analysis based on Empirical Mode Decomposition , 2008, 2008 First International Symposium on Applied Sciences on Biomedical and Communication Technologies.

[2]  Jianhua Chen,et al.  The Removal of Wall Components in Doppler Ultrasound Signals by Using the Empirical Mode Decomposition Algorithm , 2007, IEEE Transactions on Biomedical Engineering.

[3]  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.

[4]  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.

[5]  Cees Diks.  NONLINEAR DYNAMICAL SYSTEMS , 1999 .

[6]  Cornelis J. Stam,et al.  Reliable detection of nonlinearity in experimental time series with strong periodic components , 1998 .

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

[8]  Dennis Gabor,et al.  Theory of communication , 1946 .

[9]  Khaled H. Hamed,et al.  Time-frequency analysis , 2003 .

[10]  S. S. Shen,et al.  A confidence limit for the empirical mode decomposition and Hilbert spectral analysis , 2003, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[11]  Norden E. Huang,et al.  Intrinsic Mode Analysis of Human Heartbeat Time Series , 2010, Annals of Biomedical Engineering.

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

[13]  Peter J. Kyberd,et al.  EMG signal filtering based on Empirical Mode Decomposition , 2006, Biomed. Signal Process. Control..

[14]  Ph. Constantinou,et al.  On the Empirical Mode Decomposition Performance in White Gaussian Noise Biomedical Signals , 2010 .

[15]  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.

[16]  A. Karagiannis,et al.  Noise components identification in biomedical signals based on Empirical Mode Decomposition , 2009, 2009 9th International Conference on Information Technology and Applications in Biomedicine.

[17]  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.

[18]  J. Mayer,et al.  On the Quantum Correction for Thermodynamic Equilibrium , 1947 .

[19]  S. Hahn.  Hilbert Transforms in Signal Processing , 1996 .

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

[21]  Peter C. Brath,et al.  Atlas of Cardiovascular Monitoring. , 2000 .

[22]  J. A. Stewart,et al.  Nonlinear Time Series Analysis , 2015 .

[23]  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.

[24]  T.P. Pander,et al.  A suppression of an impulsive noise in ECG signal processing , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[25]  S. Mallat.  A wavelet tour of signal processing , 1998 .

[26]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

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

[28]  S C Villalobos,et al.  CRACKLE SOUNDS ANALYSIS BY EMPIRICAL MODE DECOMPOSITION , 2007 .