DOI QR코드

DOI QR Code

Lung Sound Classification Using Hjorth Descriptor Measurement on Wavelet Sub-bands

  • Rizal, Achmad (Dept. of Electrical Engineering and Information Technology, Universitas Gadjah Mada) ;
  • Hidayat, Risanuri (Dept. of Electrical Engineering and Information Technology, Universitas Gadjah Mada) ;
  • Nugroho, Hanung Adi (Dept. of Electrical Engineering and Information Technology, Universitas Gadjah Mada)
  • Received : 2017.12.13
  • Accepted : 2019.07.08
  • Published : 2019.10.31

Abstract

Signal complexity is one point of view to analyze the biological signal. It arises as a result of the physiological signal produced by biological systems. Signal complexity can be used as a method in extracting the feature for a biological signal to differentiate a pathological signal from a normal signal. In this research, Hjorth descriptors, one of the signal complexity measurement techniques, were measured on signal sub-band as the features for lung sounds classification. Lung sound signal was decomposed using two wavelet analyses: discrete wavelet transform (DWT) and wavelet packet decomposition (WPD). Meanwhile, multi-layer perceptron and N-fold cross-validation were used in the classification stage. Using DWT, the highest accuracy was obtained at 97.98%, while using WPD, the highest one was found at 98.99%. This result was found better than the multi-scale Hjorth descriptor as in previous studies.

Keywords

Activity;Complexity;Hjorth Descriptor;Lung Sound;Mobility;Wavelet Transform

References

  1. A. Bohadana, G. Izbicki, and S. S. Kraman, "Fundamentals of lung auscultation," New England Journal of Medicine, vol. 370, no. 8, pp. 744-751, 2014. https://doi.org/10.1056/NEJMra1302901
  2. J. Cohen and A. I. Zayed, Wavelets and Multiscale Analysis. New York, NY: Birkhauser, 2011.
  3. Z. Moussavi, D. Flores, and G. Thomas, "Heart sound cancellation based on multiscale products and linear prediction," in Proceedings of the 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Francisco, CA, 2004, pp. 3840-3843.
  4. L. J. Hadjileontiadis, "Wavelet-based enhancement of lung and bowel sounds using fractal dimension thresholding. Part I: Methodology," IEEE Transactions on Biomedical Engineering, vol. 52, no. 6, pp. 1143-1148, 2005. https://doi.org/10.1109/TBME.2005.846706
  5. S. Uysal, H. Uysal, B. Bolat, and T. Yildirim, "Classification of normal and abnormal lung sounds using wavelet coefficients," in Proceedings of 2014 22nd Signal Processing and Communications Applications Conference (SIU), Trabzon, Turkey, 2014, pp. 2138-2141.
  6. A. Kandaswamy, C. S. Kumar, R. P. Ramanathan, S. Jayaraman, and N. Malmurugan, "Neural classification of lung sounds using wavelet coefficients," Computers in Biology and Medicine, vol. 34, no. 6, pp. 523-537, 2004. https://doi.org/10.1016/S0010-4825(03)00092-1
  7. A. Hashemi, H. Arabalibiek, and K. Agin, "Classification of wheeze sounds using wavelets and neural networks," in Proceedings of 2011 International Conference on Biomedical Engineering and Technology, Kuala Lumpur, Malaysia, 2011, pp. 127-131.
  8. S. Abbasi, R. Derakhshanfar, A. Abbasi, and Y. Sarbaz, "Classification of normal and abnormal lung sounds using neural network and support vector machines," in Proceedings of 2013 21st Iranian Conference on Electrical Engineering (ICEE), Mashhad, Iran, 2013, pp. 1-4.
  9. M. Costa, A. L. Goldberger, and C. K. Peng, "Multiscale entropy analysis of biological signals," Physical Review E, vol. 71, no. 2, article no. 021906, 2005.
  10. M. Costa, A. L. Goldberger, and C. K. Peng, "Multiscale entropy analysis of complex physiologic time series," Physical Review Letters, vol. 89, no. 6, article no. 068102, 2002.
  11. A. Rizal, R. Hidayat, and H. A. Nugroho, "Determining lung sound characterization using Hjorth descriptor," in Proceedings of 2015 International Conference on Control, Electronics, Renewable Energy and Communications (ICCEREC), Bandung, Indonesia, 2015, pp. 54-57.
  12. A. Rizal, R. Hidayat, and H. A. Nugroho, "Multiscale Hjorth descriptor for lung sound classification," AIP Conference Proceedings, vol. 1755, no. 1, article no. 160008, 2016.
  13. A. Rizal, R. Hidayat, and H. A. Nugroho, "Lung sound classification using empirical mode decomposition and the Hjorth descriptor," American Journal of Applied Sciences, vol. 14, no. 1, pp. 166-173, 2017. https://doi.org/10.3844/ajassp.2017.166.173
  14. A. Rizal, R. Hidayat, and H. A. Nugroho, "Hjorth descriptor measurement on multidistance signal level difference for lung sound classification," Journal of Telecommunication, Electronic and Computer Engineering, vol. 9, no. 2, pp. 23-27, 2017.
  15. A. Rizal, T. L. Mengko, and A. B. Suksmono, "Lung sound recognition using wavelet packet decomposition and ART2 (Adaptive Resonance Theory 2) neural network," Proceedings of International Biomedical Engineering Day, 2006, pp. 2-6.
  16. The R.A.L.E Repository [Online]. Available: http://www.rale.ca/Repository.htm.
  17. Listen to lung sounds audio using 3M Littmann Stethoscopes [Online]. Available: http://solutions.3m.com/wps/portal/3M/en_EU/3M-Littmann-EMEA/stethoscope/littmann-learning-institute/heart-lung-sounds/lung-sounds/#introduction.
  18. D. Arnall, "Pulmonary breath sounds," 2002 [Online]. Available: http://jan.ucc.nau.edu/-daa/heartlung/breathsounds/contents.html.
  19. The Auscultation Assistant "Breath sounds," [Online]. Availabl: http://www.med.ucla.edu/wilkes/lungintro.htm.
  20. S. Reichert, R. Gass, C. Brandt, and E. Andres, "Analysis of respiratory sounds: state of the art," Clinical Medicine: Circulatory, Respiratory and Pulmonary Medicine, vol. 2, pp. 45-58, 2008.
  21. S. G. Mallat, "A theory for multiresolution signal decomposition: the wavelet representation," IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.11, no. 7, pp. 674-693, 1989. https://doi.org/10.1109/34.192463
  22. B. Hjorth, "The physical significance of time domain descriptors in EEG analysis," Electroencephalography and Clinical Neurophysiology, vol. 34, no. 3, pp. 321-325, 1973. https://doi.org/10.1016/0013-4694(73)90260-5
  23. M. Mouze-Amady and F. Horwat, "Evaluation of Hjorth parameters in forearm surface EMG analysis during an occupational repetitive task," Electroencephalography and Clinical Neurophysiology/Electromyography and Motor Control, vol. 101, no. 2, pp. 181-183, 1996. https://doi.org/10.1016/0924-980X(96)00316-5
  24. I. A. Rezek and S. J. Roberts, "Stochastic complexity measures for physiological signal analysis," IEEE Transactions on Biomedical Engineering, vol. 45, no. 9, pp. 1186-1191, 1998. https://doi.org/10.1109/10.709563
  25. X. Chen, J. Shao, Y. Long, C. Que, J. Zhang, and J. Fang, "Identification of Velcro rales based on Hilbert-Huang transform," Physica A: Statistical Mechanics and its Applications, vol. 401, pp. 34-44, 2014. https://doi.org/10.1016/j.physa.2014.01.018