Experimental respiratory signal analysis based on Empirical Mode Decomposition

Respiration is a widely used biosignal which is combined with other biosignals in order to extract information about the physiological or pathological conditions that may occur in the development of a treatment. Acquisition of respiration in a clinical environment is usually accomplished by standard hospital equipment and minimum invasive techniques. In this paper a non invasive technique is used for respiration monitoring based on accelerometers. The acquired signal is sampled and transmitted through a wireless sensor network to the gateway point (sink) where it is processed. Empirical Mode Decomposition (EMD) is considered as a method of processing of biosignals such as respiration and the application of the decomposition method in experimental signals acquired by means of a wireless sensor network is evaluated. The processing technique covered in this paper is based on selecting the appropriate signals (IMF) in which respiration is decomposed, by their spectral characteristics that correspond to respiration.

[1]  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.

[2]  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.

[3]  M. Stokes,et al.  Acoustic myographic activity increases linearly up to maximal voluntary isometric force in the human quadriceps muscle , 1991, Journal of the Neurological Sciences.

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

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

[6]  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.

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

[8]  M Solomonow,et al.  Force and surface mechanomyogram frequency responses in cat gastrocnemius. , 2000, Journal of biomechanics.

[9]  Y C Fung,et al.  Use of intrinsic modes in biology: examples of indicial response of pulmonary blood pressure to +/- step hypoxia. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[10]  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.

[11]  J.A. Fiz,et al.  Inspiratory Pressure Evaluation by means of the Entropy of Respiratory Mechanomyographic Signals , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.