Abstract
During the acquisition of lung sounds, several sources of noise can interfere with the recordings. Therefore, the detection of noise present in lung sounds plays an important role in the correct diagnosis of several pulmonary disorders such as in chronic obstructive pulmonary diseases. Denoising tools reported so far focus mainly in the detection of abnormal lung sounds from the background noise (usually vesicular background) or even just in the discrimination of normal from abnormal lung sounds. Algorithms for heart sound cancellation have also been proposed. However, it can be noticed that there is a lack of signal processing methods to efficiently detected and/or remove artifacts introduced in the acquisition environment or produced by the subject (e.g., speech). The present study focuses in the analysis of lungs sounds recorded in two different populations containing events of cough, speech and other artifacts from the surrounding environment. Feature extraction and binary classification were performed achieving, on average, values of a sensitivity and specificity ranging from 76 to 97% for the classification of cough, speech and other artifacts and from 83 to 90% for the specific detection of cough events. The detection of artifacts achieved sensitivity and specificity values of 84% and 61%, respectively for one population and 88% and 52% for another population.
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References
L. Hadjileontiadis, Empirical mode decomposition and fractal dimension filter: a novel technique for denoising explosive lung sounds, IEEE Engineering in Medicine and Biology Magazine, no. 1, pp. 30–39, Jan.
A. Bohadana, G. Izbicki, and S. S. Kraman, Fundamentals of lung auscultation. The New England journal of medicine, no. 8, pp. 744–751, Feb.
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 systematic review and meta-analysis. Respiratory medicine, no. 9, pp. 1396–1403, Sep.
L. J. Hadjileontiadis, Lung sounds: an advanced signal processing perspective. Synthesis Lectures on Biomedical Engineering, no. 1, pp. 1–100, Jan.
Z. Moussavi, Fundamentals of respiratory sounds and analysis. Synthesis Lectures on Biomedical Engineering, no. 1, pp. 1–68, Jan.
G. -C. Chang and Y. -F. Lai, Performance evaluation and enhancement of lung sound recognition system in two real noisy environments. Computer methods and programs in biomedicine, no. 2, pp. 141–150, Feb.
T. H. Falk and W. -Y. Chan, Modulation filtering for heart and lung sound separation from breath sound recordings. Conference proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, pp. 1859–1862, Jan.
A. Yadollahi and Z. Moussavi, On arithmetic misconceptions of spectral analysis of biological signals, in particular respiratory sounds. Conference proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, pp. 388–391, Jan.
E. Kvedalen, Signal processing using the Teager Energy Operator and other nonlinear operators, Jul.
L. Mendes, P. Carvalho, C. A. Teixeira, R. P. Paiva, and J. Henriques, Robust features for detection of crackles: an exploratory study. Conference proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, pp. 1473–6, Aug.
L. Hadjileontiadis and I. Rekanos, Detection of explosive lung and bowel sounds by means of fractal dimension, IEEE Signal Processing Letters, no. 10, pp. 311–314, Oct.
S. Charleston-Villalobos, G. Dorantes-Méndez, R. González-Camarena, G. Chi-Lem, J. G. Carrillo, and T. Aljama-Corrales, Acoustic thoracic image of crackle sounds using linear and nonlinear processing techniques. Medical & biological engineering & computing, no. 1, pp. 15–24, Jan.
M. Bahoura, Pattern recognition methods applied to respiratory sounds classification into normal and wheeze classes, Computers in Biology and Medicine, no. 9, pp. 824–843, Sep.
G. Brown, A. Pocock, M. -J. Zhao, and M. Luján, Conditional likelihood maximisation: a unifying framework for information theoretic feature selection, The Journal of Machine Learning Research, no. 1, pp. 27–66, Jan.
D. Nunes, A. Leal, R. Couceiro, J. Henriques, L. Mendes, P. Carvalho, and C. Teixeira, A low-complex multi-channel methodology for noise detection in phonocardiogram signals, 37th Annual International Conference of the Ieee Engineering in Medicine and Biology Society, p. 4.
C. Lucio, C. Teixeira, J. Henriques, P. de Carvalho, and R. P. Paiva, Voluntary cough detection by internal sound analysis, 2014 7th International Conference on Biomedical Engineering and Informatics. 1em plus 0.5em minus 0.4em IEEE, pp. 405–409, Oct.
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The authors acknowledge the financial support of the EU Project WELCOME (FP7-611223).
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Leal, A. et al. (2019). A Multi-feature Approach for Noise Detection in Lung Sounds. In: Zhang, YT., Carvalho, P., Magjarevic, R. (eds) International Conference on Biomedical and Health Informatics. ICBHI 2015. IFMBE Proceedings, vol 64. Springer, Singapore. https://doi.org/10.1007/978-981-10-4505-9_47
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DOI: https://doi.org/10.1007/978-981-10-4505-9_47
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