Multiscale tsallis entropy for pulmonary crackle detection

(1) * Achmad Rizal Mail (Dept. of Electrical Engneering & Information Technology, Universitas Gadjah Mada, Yogyakarta, Indonesia)
(2) Risanuri Hidayat Mail (Dept. of Electrical Engneering & Information Technology, Universitas Gadjah Mada, Yogyakarta, Indonesia)
(3) Hanung Adi Nugroho Mail (Dept. of Electrical Engneering & Information Technology, Universitas Gadjah Mada, Yogyakarta, Indonesia)
*corresponding author

Abstract


Abnormalities in the lungs can be detected from the sound produced by the lungs. Diseases that occur in the lungs or respiratory tract can produce a distinctive lung sound. One of the examples of the lung sound is the pulmonary crackle caused by pneumonia or chronic bronchitis. Various digital signal processing techniques are developed to detect pulmonary crackle sound automatically, such as the measurement of signal complexity using Tsallis entropy (TE). In this study, TE measurements were performed through several orders on the multiscale pulmonary crackle signal. The pulmonary crackle signal was decomposed using the coarse-grained procedure since the lung sound as the biological signal had a multiscale property. In this paper, we used 21 pulmonary crackle sound and 22 normal lung sound for the experiment. The results showed that the second order TE on the scale of 1-15 had the highest accuracy of 97.67%. This result was better compared to the use of multi-order TE from the previous study, which resulted in an accuracy of 95.35%.

Keywords


Tsallis entropy; Lung sound; Pulmonary crackle; Multiscale; Multilayer perceptron

   

DOI

https://doi.org/10.26555/ijain.v4i3.273
      

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References


[1] X. Lu and M. Bahoura, “An integrated automated system for crackles extraction and classification,” Biomedical Signal Processing and Control, vol. 3, pp. 244–254, 2008, doi: https://doi.org/10.1016/j.bspc.2008.04.003.

[2] G. Serbes, C. O. Sakar, Y. P. Kahya, and N. Aydin, “Pulmonary crackle detection using time-frequency and time-scale analysis,” Digital Signal Processing: A Review Journal, vol. 23, no. 3, pp. 1012–1021, 2013, doi: https://doi.org/10.1016/j.dsp.2012.12.009.

[3] H. Melbye, “Auscultation of the lungs: still a useful examination?,” Tidsskrift for den Norske laegeforening : tidsskrift for praktisk medicin, ny raekke., vol. 121, no. 4, pp. 451–454, 2001, available at: https://europepmc.org/abstract/med/11255861.

[4] M. Yeginer and Y. P. Kahya, “Feature extraction for pulmonary crackle representation via wavelet networks,” Computers in Biology and Medicine, vol. 39, no. 8, pp. 713–721, 2009, doi: https://doi.org/10.1016/j.compbiomed.2009.05.008.

[5] V. I. Quandt, E. R. Pacola, S. F. Pichorim, and H. R. Gamba, “Pulmonary crackle characterization : approaches in the use of discrete wavelet transform regarding border effect , mother-wavelet selection , and subband reduction,” Research on Biomedical Engineering, vol. 31, no. 2, pp. 148–159, 2015, doi: https://doi.org/10.1590/2446-4740.0639.

[6] A. Rizal, R. Hidayat, and H. A. Nugroho, “Multiscale Hjorth descriptor for lung sound classification,” AIP Conference Proceedings, vol. 1755, no 160008, pp. 1-7, 2016, doi: https://doi.org/10.1063/1.4958601.

[7] J. Gnitecki and Z. Moussavi, “The fractality of lung sounds: A comparison of three waveform fractal dimension algorithms,” Chaos, Solitons & Fractals, vol. 26, no. 4, pp. 1065–1072, Nov. 2005, doi: https://doi.org/10.1016/j.chaos.2005.02.018.

[8] A. Mondal, P. Bhattacharya, and G. Saha, “Detection of lungs status using morphological complexities of respiratory sounds.,” The Scientific World Journal, vol. 2014, pp. 1829–1838, Jan. 2014, doi: https://doi.org/10.1155/2014/182938.

[9] M. Molaie, S. Jafari, M. H. Moradi, J. C. Sprott, and S. M. R. H. Golpayegani, “A chaotic viewpoint on noise reduction from respiratory sounds,” Biomedical Signal Processing and Control, vol. 10, pp. 245–249, Mar. 2014, doi: https://doi.org/10.1016/j.bspc.2013.10.009.

[10] M. Costa, A. L. Goldberger, and C. K. Peng, “Multiscale entropy analysis of biological signals,” Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, vol. 71, pp. 1–18, 2005, doi: https://doi.org/10.1103/PhysRevE.71.021906.

[11] C. Tsallis, “Possible generalization of Boltzman-Gibbs Statistics,” Journal of Statistical Physics, vol. 52, no. 1/2, pp. 479–487, 1988, doi: https://doi.org/10.1007/BF01016429.

[12] U. R. Acharya, H. Fujita, V. K. Sudarshan, S. Bhat, and J. E. W. Koh, “Application of entropies for automated diagnosis of epilepsy using EEG signals : A review,” Knowledge-Based Systems, vol. 88, pp. 85–96, 2015, doi: https://doi.org/10.1016/j.knosys.2015.08.004.

[13] C. Sridhar, U. R. Acharya, H. Fujita, and G. M. Bairy, “Automated diagnosis of Coronary Artery Disease using nonlinear features extracted from ECG signals,” Proc. 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE Press, Oct. 2016, pp. 000545–000549, doi: https://doi.org/10.1109/SMC.2016.7844296.

[14] M. Fernandez-Granero, D. Sanchez-Morillo, and A. Leon-Jimenez, “Computerised Analysis of Telemonitored Respiratory Sounds for Predicting Acute Exacerbations of COPD,” Sensors, vol. 15, no. 10, pp. 26978–26996, Oct. 2015, doi: https://doi.org/10.3390/s151026978.

[15] A. Rizal, R. Hidayat, and H. A. Nugroho, “Pulmonary Crackle Feature Extraction using Tsallis Entropy for Automatic Lung Sound Classification,” Proc. The 1st 2016 International Conference on Biomedical Engineering (iBioMed), Oct. 2016, pp. 8–11, doi: https://doi.org/10.1109/IBIOMED.2016.7869823.

[16] A. Bohadana, G. Izbicki, and S. S. Kraman, “Fundamentals of lung auscultation.,” The New England journal of medicine, vol. 370, no. 8, pp. 744–51, Feb. 2014, doi: https://doi.org/10.1056/NEJMra1302901.

[17] S. Reichert, R. Gass, C. Brandt, and E. Andrès, “Analysis of Respiratory Sounds : State of the Art,” Clinical medicine. Circulatory, respiratory and pulmonary medicine, vol. 2008, no. 2, pp. 45–58, 2008, doi: https://doi.org/10.4137/CCRPM.S530.

[18] J. Angulo and F. Esquivel, “Multifractal Dimensional Dependence Assessment Based on Tsallis Mutual Information,” Entropy, vol. 17, no. 8, pp. 5382–5401, Jul. 2015, doi: https://doi.org/10.3390/e17085382.

[19] R. Palaniappan, Biological Signal Analysis. 1st ed. Frederiksberg: Ventus Publishing ApS, 2010, available at: Google Scholar.

[20] S. Charleston-Villalobos, L. Albuerne-Sanchez, R. Gonzalez-Camarena, M. Mejia-Avila, G. Carrillo-Rodriguez, and T. Aljama-Corrales, “Linear and Nonlinear Analysis of Base Lung Sound in Extrinsic Allergic Alveolitis Patients in Comparison to Healthy Subjects,” Methods of Information in Medicine, vol. 52, no. 3, pp. 266–276, Apr. 2013, doi: https://doi.org/10.3414/ME12-01-0037.

[21] A. Rizal, R. Hidayat, and H. A. Nugroho, “Determining Lung Sound Characterization Using Hjorth Descriptor,” Proc.2015 International Conference on Control, Electronics, Renewable Energy and Communication (ICCEREC), IEEE Press, Aug. 2015, pp. 20–23, doi: https://doi.org/10.1109/ICCEREC.2015.7337053.

[22] A. Rizal, R. Hidayat, and H. A. Nugroho, “Multi-scale Grey-Level Difference for Lung Sound Classification,” Journal of Electrical Systems, vol. 13, no. 3, pp. 556–564, Sept. 2016, available at: http://journal.esrgroups.org/jes/papers/12_3_9.pdf.

[23] A. Rizal, R. Hidayat, and H. A. Nugroho, “Entropy Measurement as Features Extraction in Automatic Lung Sound Classification” Proc. 2017 International Conference on Control, Electronics, Renewable Energy and Communication (ICCEREC), IEEE Press, Sept. 2015, doi: https://doi.org/10.1109/ICCEREC.2017.8226668.

[24] A. Rizal, R. Hidayat, and H. A. Nugroho, “Fractal Dimension for Lung Sound Classification in Multiscale Scheme,” Journal of Computer Science, vol. 14, no. 8, pp. 1081–1096, Aug. 2018, available at: https://thescipub.com/abstract/10.3844/jcssp.2018.1081.1096.

[25] A. Rizal, R. Hidayat, and H. A. Nugroho, “Comparison of Multiscale Entropy Techniques for Lung Sound Classification,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 12, no. 3, pp. 984–994, 2018, doi: https://doi.org/10.11591/ijeecs.v12.i3.pp984-994.

[26] D. Emmanouilidou and M. Elhilali, “Characterization of Noise Contaminations in Lung Sound Recordings,” Proc. 35th Annual International Conference of the IEEE EMBS, 2013, pp. 2551–2554, doi: https://doi.org/10.1109/EMBC.2013.6610060.

[27] Z. Moussavi, Fundamentals of Respiratory Sounds and Analysis, 1st ed, Morgan & Claypool Publisher, 2006, doi: https://doi.org/10.2200/S00054ED1V01Y200609BME008.

[28] M. F. Syahputra, S. I. G. Situmeang, R. F. Rahmat, and R. Budiarto, “Noise Reduction in Breath Sound Files Using Wavelet Transform Based Filter,” IOP Conf. Series: Materials Science and Engineering, 2017, vol. 190, p. 012040, doi: https://doi.org/10.1088/1757-899X/190/1/012040.

[29] M. Aboofazeli and Z. Moussavi, “Automated Classification of Swallowing and Breath Sounds,” Proc. Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2004, 2004, pp. 3816–3819, doi: https://doi.org/10.1109/IEMBS.2004.1404069.

[30] M. Molaie, S. Jafari, M. H. Moradi, J. C. Sprott, and S. M. R. H. Golpayegani, “A chaotic viewpoint on noise reduction from respiratory sounds,” Biomedical Signal Processing and Control, vol. 10, pp. 245–249, Mar. 2014, doi: https://doi.org/10.1016/j.bspc.2013.10.009.




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