Lung Function Classification of Smartphone Recordings - Comparison of Signal Processing and Machine Learning Combination Sets

João Teixeira, Luís Teixeira, João Fonseca, Tiago Jacinto

2015

Abstract

Worldwide, over 250 million people are affected by chronic lung conditions such as Asthma and COPD. These can cause breathlessness, a harsh decrease in quality of life and, if not detected and duly managed, even death. In this paper, we aim to find the best and most efficient combination of signal processing and machine learning approaches to produce a smartphone application that could accurately classify lung function, using microphone recordings as the only input. A total of 61 patients performed the forced expiration maneuver providing a dataset of 101 recordings. The signal processing comparison experiments were conducted in a backward selection approach, reducing from 54 to 12 final envelopes, per recording. The classification experiments focused first on differentiating Normal from Abnormal lung function, and second in multiple lung function patterns. The results from this project encourage further development of the system.

References

  1. Bland, J. M. and Altman, D. G. (1986). Statistical methods for assessing agreement between two methods of clinical measurement. Lancet, 1(8476):307-10.
  2. Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2):123-140.
  3. Breiman, L. (2001). Random forests. Machine learning, 45(1):5--32.
  4. Buntine, W. (1992). Learning classification trees. Statistics and Computing, 2(2):63-73.
  5. Freund, Y. and Schapire, R. E. (1996). Experiments with a new boosting algorithm. In Saitta, L., editor, ICML, pages 148-156, Bari, Italy. Morgan Kaufmann.
  6. Larson, E. C., Goel, M., Boriello, G., Heltshe, S., Rosenfeld, M., and Patel, S. N. (2012). SpiroSmart: Using a Microphone to Measure Lung Function on a Mobile Phone. In 14th ACM International Conference on Ubiquitous Computing, page 10, Pittsburgh, Pennsylvania, USA.
  7. Liang, H., Lukkarinen, S., and Hartimo, I. (1997). Heart sound segmentation algorithm based on heart sound envelogram. In Computers in Cardiology 1997, volume 24, pages 105-108. IEEE.
  8. Miller, M. R., Hankinson, J., Brusasco, V., Burgos, F., Casaburi, R., Coates, A., Crapo, R., Enright, P., van der Grinten, C. P. M., Gustafsson, P., Jensen, R., Johnson, D. C., MacIntyre, N., McKay, R., Navajas, D., Pedersen, O. F., Pellegrino, R., Viegi, G., and Wanger, J. (2005). Standardisation of spirometry. The European respiratory journal, 26(2):319-38.
  9. Pierce, R. (2005). Spirometry: an essential clinical measurement. Australian family physician, 34(7):535-9.
  10. Russel, S. and Norvig, P. (2002). Artificial Intelligence: A Modern Approach. Prentice Hall, 2nd edition.
  11. Savitzky, A. and Golay, M. J. E. (1964). Smoothing and differentiation of data by simplified least squares procedures. Analytical chemistry, 36(8):1627-1639.
  12. van Stein, B. (2013). A Mobile Smart Care platform Home spirometry by using the smartphone microphone. Master's thesis, Leiden University, Leiden, The Netherlands.
  13. Vapnik, V. N. (1999). An overview of statistical learning theory. IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council, 10(5):988-99.
  14. Wakita, H. (1973). Direct estimation of the vocal tract shape by inverse filtering of acoustic speech waveforms. Audio and Electroacoustics, IEEE Transactions on, 21(5):417-427.
  15. World Health Organization (2013a). Asthma: Fact sheet N307. http://www.who.int/mediacentre/factsheets/fs307/en/ . Accessed: 26-06-2014.
  16. World Health Organization (2013b). Chronic obstructive pulmonary disease (COPD): Fact sheet N315. http://www.who.int/mediacentre/factsheets/fs315/en/ . Accessed: 26-06-2014.
  17. Xu, W., Huang, M.-c., Liu, J. J., Ren, F., Shen, X., Liu, X., and Sarrafzadeh, M. (2013). mCOPD. In Proceedings of the 6th International Conference on Pervasive Technologies Related to Assistive Environments - PETRA 7813, PETRA 7813, pages 1-8, New York, New York, USA. ACM Press.
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Paper Citation


in Harvard Style

Teixeira J., Teixeira L., Fonseca J. and Jacinto T. (2015). Lung Function Classification of Smartphone Recordings - Comparison of Signal Processing and Machine Learning Combination Sets . In Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2015) ISBN 978-989-758-068-0, pages 123-130. DOI: 10.5220/0005222001230130


in Bibtex Style

@conference{healthinf15,
author={João Teixeira and Luís Teixeira and João Fonseca and Tiago Jacinto},
title={Lung Function Classification of Smartphone Recordings - Comparison of Signal Processing and Machine Learning Combination Sets},
booktitle={Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2015)},
year={2015},
pages={123-130},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005222001230130},
isbn={978-989-758-068-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2015)
TI - Lung Function Classification of Smartphone Recordings - Comparison of Signal Processing and Machine Learning Combination Sets
SN - 978-989-758-068-0
AU - Teixeira J.
AU - Teixeira L.
AU - Fonseca J.
AU - Jacinto T.
PY - 2015
SP - 123
EP - 130
DO - 10.5220/0005222001230130