Abstract
This study presents detection of pulmonary disorders using different spectral analysis methods such as fast Fourier transform, autoregressive and the autoregressive moving average. Power spectral densities of the sounds were estimated through these methods. Feature vectors were constructed by extracting statistical features from the PSDs. Created feature vectors were used as inputs into the artificial neural networks. Then performances of spectral analysis methods were compared according to classification accuracies, sensitivities and specificities. In this aspect, the study is a comparative study of different spectral analysis methods.
Article PDF
Avoid common mistakes on your manuscript.
References
A. Kandaswamy, C. S. Kumar, R. P. Ramanathan, S. Jayaraman and N. Malmurugan, Neural classification of lung sounds using wavelet coefficients, Comput. Biol. Med., 34 (2004), pp. 523–537.
B. Sankur, Y.P. Kahya, E.C. Guler and T. Engin, Comparison of AR-Based algorithms for respiratory sounds classification, Comput. Biol. Med., 24(1) (1994), pp. 67–76.
P. Forgacs, Lung sounds, Baillere Tindall, (London 1978).
A. Gurung, C. G. Scrafford, J. M. Tielsch, O. S. Levine and W. Checkley, Computerized lung sound analysis as diagnostic aid for the detection of abnormal lung sounds: A system-atic review and meta-analysis, Resp. Med., 105 (2011), pp. 1396–1403.
B. Mohammed, Pattern recognition methods applied to respiratory sounds classification into normal and wheeze classes, Comput. Biol. Med., 39 (2009), pp. 824–843.
M. Bahoura and X. Lu, Separation of crackles from vesicular sounds using wavelet packet transform, Acoustics, Speech and Signal Processing ICASSP, 2, (2006), pp. 1076–9.
F.Z. Gogus, B. Karlık and G. Harman, Classification of asthmatic breath sounds by using wavelet transforms and neural networks, International Journal of Signal Processing Systems, 3(2) (2015), pp. 106–111.
G. Harman, Comparision of different feature extraction methods to analysis lung sound signals, Master Thesis, The Graduate Institute of Sciences and engineering, (Fatih University 2010).
K.E.Forkheim, D.Scuse, H.Pasterkamp, A comparison of neural network models for wheeze detection, in: IEEE WESCANEX 95 Proceedings, 1, (New York, USA 1995), pp. 214–219.
S. Rietveld, M. Oud and E.H. Dooijes, Classification of asthmatic breath sounds: prelimi-nary results of the classifying capacity of human examiners versus artificial neural networks, Comput. Biol. Med., 32 (1999), pp. 440–448.
L.R. Waitman, K.P. Clarkson, J.A. Barwise and P.H. King, Representation and classification of breath sounds recorded in an intensive care setting using neural networks, J Clin Monit., 16 (2000), pp. 95–105.
I. Guler, H. Polat and U. Ergun, Combining neural network and genetic algorithm for pre-diction of lung sounds, J Med Syst, 29(3) (2005), pp. 217–231.
N. Gavriely, Y. Palti and G. Alroy, Spectral characteristics of normal breath sounds, J. appl. Physiol., 50 (1981), pp. 307–314.
H. J. Scheurer, J. Vanderschoot, A. H. Zwinderman, J. H. Dijkman and P. J. Sterk, Abnormal lung sounds in patients with asthma during episodes with normal lung function, Chest, 106(1) (1994), pp. 91–99.
S. K. Chowdhury and A. K. Majumder, Digital spectrum analysis of respiratory sound, IEEE Trans Biomed Eng., 28(11) (1981), pp. 784.
M. Oud, E.H. Dooijes, S. Jaring and V.D. Zee, Asthmatic airways obstruction assessment based on detailed analysis of respiratory sound spectra, IEEE Trans Biomed Eng., 47(11) (2000), pp. 1450–1455.
S.M. Kay and S.L. Marple, Spectrum analysis—a modern perspective, Proc. IEEE, 69 (11) (1981), pp. 1380–1419.
I. Sen, M. Saraclar and Y. P. Kahya, A comparison of SVM and GMM-based classifier configurations for diagnostic classification of pulmonary sounds, IEEE Trans Biomed Eng., 62 (7) (2015), pp. 1768–1776.
S. Alsmadi and Y. P. Kahya, Design of a DSP-based instrument for realtime classification of pulmonary sounds, Comput. Biol. Med., 38 (1) (2008), pp.53–61.
S. Charleston-Villalobos, G. Martinez-Hernandez, R. Gonzalez-Camarena, G. Chi-Lem, J.G. Carrillo and T. Aljama-Corrales, Assessment of multichannel lung sounds parameterization for two-class classification in interstitial lung disease patients, Comput. Biol. Med., 41(7) (2011), pp. 473–482.
E. C. Güler, B. Sankur, Y. P. Kahya and S. Raudys, Two-stage classification of respiratory sound patterns, Comput. Biol. Med., 35 (1) (2005), pp. 67–83.
H. Pasterkamp, R. E. Powell, and I. Sanchez, Lung sound spectra at standardized air Clow in normal infants, children and adults, Am J Respir Crit Care Med, (1996), 154(2), pp. 424–430.
P. M. T. Broersen and S. Waele, Detection of methacholine with time series models of lung sounds, IEEE Trans. Instrum. Meas., 49(3) (2000), pp. 517–523.
I. Sen and Y. P. Kahya, A Multi-channel analog processing circuit for respiratory sound acquisition applications, Proceedings of the 25.Annual lntemational Conference of the IEEE EMBS, 4 (2003), pp. 3192–3195.
M. Yeginer and Y.P. Kahya, Feature extraction for pulmonary crackle representation via wavelet networks, Comput. Biol. Med., 39 (2009), pp. 713–721.
F.Z. Gogus, Analysis and classification of biomedical sounds, Master Thesis, The Graduate School of Natural and Applied Science, (Selcuk University 2015).
S.V. Vaseghi, Power spectrum and correlation, in: Advanced Digital Signal Processing and Noise Reduction, third edition, John Wiley & Sons, Ltd, (Chichester UK 2006).
M. Cerna and A. F. Harvey, (2000), ‘The fundamentals of FFT-based signal analysis and measurement, http://www.ni.com/white-paper/4278/en/. Accessed 15 December 2015.
J. G. Proakis and D. G. Monolakis, Digital signal processing principles, algorithms, and applications, Prentice Hall, (New Jersey 1996).
R. Kızılaslan and B. Karlık, Combination neural networks forecasters for monthly natural gas consumption prediction, Neural Netw World, 19 (2) (2009), pp. 191–199.
R.O. Duda, P.E. Hart and D.G. Stork, Pattern classification and scene analysis, Wiley, (New York 2001).
T. Villmann, Neural network approaches in medicine—a review of actual developments, in: Proceedings of the European Symposium on Artificial Neural Networks (ESANN 2000), (Bruges Belgium 2000), pp. 165–176.
F. Gurgen, Neural network based decision making in diagnostic applications, IEEE Eng. Med. Biol. Mag., 18 (4) (1999), pp. 89–93.
P.J.G. Lisboa, A review of evidence of health benefit from artificial neural networks in medical intervention, Neural Networks, 15 (1) (2002), pp. 11–39.
J. Demšar, T. Curk, A.Erjavec, Č. Gorup, T. Hočevar, M. Milutinovič, M. Možina, M. Po-lajnar, M. Toplak, A. Starič, M. Stajdohar, L. Umek, L. Žagar, J. Žbontar, M. Žitnik and B. Zupan, Orange: data mining toolbox in Python,. JMLR, 14 (1) (2013), pp. 2349–2353.
L. Tomak and Y. Bek, The analysis of receiver operating characteristic curve and comparison of the areas under the curve, Journal of Experimental and Clinical Medicine, 27 (2) (2010), pp. 58–65.
J. A. Hanley and B. J. McNeil, The meaning and use of the area under a receiver operating characteristic (ROC) curve, Radiology, 143 (1982), pp. 29–36.
W. M. Grove, Mathematıcal aspects of diagnosis. United States of America, (2006), pp. 50–75.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).
About this article
Cite this article
Göğüş, F.Z., Karlık, B. & Harman, G. Identification of Pulmonary Disorders by Using Different Spectral Analysis Methods. Int J Comput Intell Syst 9, 595–611 (2016). https://doi.org/10.1080/18756891.2016.1204110
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1080/18756891.2016.1204110