Advertisement

Wheeze Detection Using Convolutional Neural Networks

  • Kirill Kochetov
  • Evgeny Putin
  • Svyatoslav Azizov
  • Ilya Skorobogatov
  • Andrey Filchenkov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10423)

Abstract

In this paper, we propose to use convolutional neural networks for automatic wheeze detection in lung sounds. We present convolutional neural network based approach that has several advantages compared to the previous approaches described in the literature. Our method surpasses the standard machine learning models on this task. It is robust to lung sound shifting and requires minimal feature preprocessing steps. Our approach achieves 99% accuracy and 0.96 AUC on our datasets.

Keywords

Wheeze detection Convolutional neural networks Machine learning Deep learning 

References

  1. 1.
    Reichert, S., Raymond, G., Christian, B., Andrès, E.: Analysis of respiratory sounds: state of the art. Clin. Med. Circ. Respirat. Pulm. Med. 2, 45–58 (2008)Google Scholar
  2. 2.
    Bahoura, M., Lu, X.: Separation of crackles from vesicular sounds using wavelet packet transform. In: 2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings (2006)Google Scholar
  3. 3.
    Yann, L., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)CrossRefGoogle Scholar
  4. 4.
    Farabet, C., Couprie, C., Najman, L., LeCun, Y.: Learning hierarchical features for scene labeling. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1915–1929 (2013)CrossRefGoogle Scholar
  5. 5.
    Tompson, J.J., Jain, A., LeCun, Y., Bregler, C.: Joint training of a convolutional network and a graphical model for human pose estimation. In: Advances in Neural Information Processing Systems, pp. 1799–1807 (2014)Google Scholar
  6. 6.
    Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)Google Scholar
  7. 7.
    Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12(Aug), 2493–2537 (2011)zbMATHGoogle Scholar
  8. 8.
    Hu, B., Lu, Z., Li, H., Chen, Q.: Convolutional neural network architectures for matching natural language sentences. In: Advances in Neural Information Processing Systems, pp. 2042–2050 (2014)Google Scholar
  9. 9.
    Bordes, A., Chopra, S., Weston, J.: Question answering with subgraph embeddings. arXiv preprint arXiv:1406.3676 (2014)
  10. 10.
    Palaz, D., Magimai-Doss, M., Collobert, R.: Analysis of CNN-based speech recognition system using raw speech as input. In: Proceedings of the 16th Annual Conference of International Speech Communication Association (Interspeech), pp. 11–15 (2015)Google Scholar
  11. 11.
    Mikolov, T., Deoras, A., Povey, D., Burget, L., Černocký, J.: Strategies for training large scale neural network language models. In: 2011 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), pp. 196–201 (2011)Google Scholar
  12. 12.
    Hinton, G., Deng, L., Yu, D., Dahl, G.E., Mohamed, A.R., Jaitly, N., Kingsbury, B.: Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Sig. Process. Mag. 29(6), 82–97 (2012)CrossRefGoogle Scholar
  13. 13.
    Sainath, T.N., Mohamed, A.R., Kingsbury, B., Ramabhadran, B.: Deep convolutional neural networks for LVCSR. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8614–8618 (2013)Google Scholar
  14. 14.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems (2012)Google Scholar
  15. 15.
    Zhang, H., McLoughlin, I., Song, Y.: Robust sound event recognition using convolutional neural networks. In: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 559–563 (2015)Google Scholar
  16. 16.
    Zhang, J., Ser, W., Yu, J., Zhang, T.: A novel wheeze detection method for wearable monitoring systems. In: 2009 International Symposium on Intelligent Ubiquitous Computing and Education (2009)Google Scholar
  17. 17.
    Mayorga, P., Druzgalski, C., Morelos, R., Gonzalez, O., Vidales, J.: Acoustics based assessment of respiratory diseases using GMM classification. In: 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology (2010)Google Scholar
  18. 18.
    Milicevic, M., Mazic, I., Bonkovic, M.: Classification accuracy comparison of asthmatic wheezing sounds recorded under ideal and real-world conditions. In: 15th International Conference on Artificial Intelligence, Knowledge Engineering and Databases (AIKED 2016), Venice (2016)Google Scholar
  19. 19.
    Bahoura, M., Pelletier, C.: Respiratory sounds classification using cepstral analysis and Gaussian mixture models. In: The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (2004)Google Scholar
  20. 20.
    Palaniappan, R., Sundaraj, K., Sundaraj, S.: A comparative study of the SVM and K-nn machine learning algorithms for the diagnosis of respiratory pathologies using pulmonary acoustic signals. BMC Bioinform. 15, 223 (2014)CrossRefGoogle Scholar
  21. 21.
    Wrigley, D.: Heart and Lung Sounds Reference Library. PESI HealthCare, Eau Claire (2011)Google Scholar
  22. 22.
    Shaharum, S., Sundaraj, K., Palaniappan, R.: A survey on automated wheeze detection systems for asthmatic patients. Bosnian J. Basic Med. Sci. 12, 249 (2012)Google Scholar
  23. 23.
    Wei, H., Chan, C., Choy, C., Pun, P.: An efficient MFCC extraction method in speech recognition. In: Circuits and Systems (2006)Google Scholar
  24. 24.
    Ciresan, D.C., Meier, U., Masci, J., Gambardella, L.M., Schmidhuber, J.: High-performance neural networks for visual object classification. arXiv preprint arXiv:1102.0183 (2011)
  25. 25.
    Liaw, A., Wiener, M.: Classification and regression by randomForest. R News 2(3), 18–22 (2002)Google Scholar
  26. 26.
    Friedman, J.H.: Greedy function approximation: a gradient boosting machine. In: Annals of Statistics, pp. 1189–1232 (2001)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Kirill Kochetov
    • 1
  • Evgeny Putin
    • 1
  • Svyatoslav Azizov
    • 1
  • Ilya Skorobogatov
    • 2
  • Andrey Filchenkov
    • 1
  1. 1.Computer Technologies LabITMO UniversitySt. PetersburgRussia
  2. 2.Center for Billing Technologies and Printing ServicesSt. PetersburgRussia

Personalised recommendations