EMD based Hilbert energy spectrum analysis of adventitious lung wave signals

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    The respiratory adventitious waves are analyzed effectively by time frequency analysis. In this paper, we present a new approach for rectifying the abnormality in adventitious wave. Basically, there are    two types of respiratory sound waves and these are classified as wheezes and crackles. The proposed method utilizes the time frequency analysis using spectrum analysis method. The modified Empirical Mode Decomposition (EMD) called Ensemble Empirical Mode Decomposition (EEMD) to plot energy spectrum of adventitious wave is used in this work. The proposed method decomposes the respiratory adventitious wave into a different Intrinsic Mode Function (IMF). The long and short duration adventitious waves are present in a wheezing subject and this leads to production of non stationary and nonlinear sound waves. The empirical mode decomposition (EMD) decomposes such characteristic waves. The available spectrogram analyzes techniques related to additive expansions and explore amplitude wise time-frequency distribution. The methodology discussed in this context responding greatly even for correlative noise and explores energy spectra in addition to amplitude spectra.  The various IMFs such produced are exhibits the fine details of adventitious wave and thus pattern can be predicted for final residual. The energy spectrum can be viewed as a diagnostic tool for accurate analysis of wheezing pattern. The decomposed frequency patterns indicate the physiological aspects. The instantaneous frequency and Hilbert energy spectrum based on above mentioned a method are employed by IMF to analyze and present the result in time-frequency distribution to explore the characteristics of inherent properties adventitious signals. The Hilbert marginal spectrum has been used to indicate overall energy distribution from each frequency component. Finally, the resultant EMD analysis along with EEMD energy spectrum is better for asthmatic subject and solves mode mixing problems.


  • Keywords


    CEEMDGN; EEMD; CASRSW; DASRSW

  • References


      [1] Lozano, Manuel, José Antonio Fiz, and Raimon Jané. "Performance Evaluation of the Hilbert–Huang Transform for Respiratory Sound Analysis and Its Application to Continuous Adventitious Sound Characterization." Signal Processing 120 (2016): 99-116. Web.

      [2] Colominas, Marcelo A., Gastón Schlotthauer, and María E. Torres. "Improved Complete Ensemble EMD: A Suitable Tool for Biomedical Signal Processing." Biomedical Signal Processing and Control 14 (2014): 19-29. Web

      [3] Wu, Zhaohua, and Norden E. Huang. "Ensemble Empirical Mode Decomposition: A Noise-Assisted Data Analysis Method." Advances in Adaptive Data Analysis 01.01 (2009): 1-41. Web.

      [4] Tabloid, S. A., & Hadjileontiadis, L. J. (2007). Wheeze detection based on time-frequency analysis of breath sounds. Computers in Biology and Medicine, 37 (8), 1073-1083. doi:10.1016/j. compbiomed.2006.09.007

      [5] Yamashita, M., Himeshima, M., & Matsunaga, S. (2014). Robust classification between normal and abnormal lung sounds using adventitious-sound and heart-sound models. 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Do: 10.1109/icassp. 2014.6854437

      [6] Huang, N. E., Hu, K., Yang, A. C., Chang, H., Jia, D., Liang, W.,Wu, Z. (2016). On Holo-Hilbert spectral analysis: A full informational spectral representation for nonlinear and non-stationary data. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374 (2065), 20150206. Do: 10.1098/rsta. 2015.0206

      [7] Karagiannis, A.; Constantinou, P.;, "Investigating performance of Empirical Mode Decomposition application on electrocardiogam," Biomedical Engineering Conference (CIBEC), 2010th Cairo International, vol., no., 1 4Dec. 2010 doi:CIBEC.2010.5716048

      [8] T. Padmapriya and V. Saminadan, “Priority based fair resource allocation and Admission Control Technique for Multi-user Multi-class downlink Traffic in LTE-Advanced Networks”, International Journal of Advanced Research, vol.5, no.1, pp.1633-1641, January 2017.

      [9] S.V.Manikanthan and K.srividhya "An Android based secure access control using ARM and cloud computing", Published in: Electronics and Communication Systems (ICECS), 2015 2nd International Conference on 26-27 Feb. 2015,Publisher: IEEE,DOI: 10.1109/ECS.2015.7124833.

      [10] M. Rajesh, Manikanthan, “GET-UP-AND-GO EFFICIENT MEMETIC ALGORITHM BASED AMALGAM ROUTING PROTOCOL”, International Journal of Pure and Applied Mathematics, ISSN NO:1314-3395, Vol-116, No. 21, Oct 2017.

      [11] Rajesh, M., and J. M. Gnanasekar. "Path observation-based physical routing protocol for wireless ad hoc networks." International Journal of Wireless and Mobile Computing 11.3 (2016): 244-257.


 

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Article ID: 9133
 
DOI: 10.14419/ijet.v7i1.5.9133




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