Abstract
In this paper, we propose a novel architecture called noise masking recurrent neural network (NMRNN) for lung sound classification. The model jointly learns to extract only important respiratory-like frames without redundant noise and then by exploiting this information is trained to classify lung sounds into four categories: normal, containing wheezes, crackles and both wheezes and crackles. We compare the performance of our model with machine learning based models. As a result, the NMRNN model reaches state-of-the-art performance on recently introduced publicly available respiratory sound database.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Bahoura, M., Pelletier, C.: Respiratory sounds classification using cepstral analysis and Gaussian mixture models. In: 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEMBS 2004, vol. 1, pp. 9–12. IEEE (2004)
Mayorga, P., Druzgalski, C., Morelos, R.L., Gonzalez, O.H., Vidales, J.: Acoustics based assessment of respiratory diseases using GMM classification. In: 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 6312–6316. IEEE (2010)
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(1), 223 (2014)
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)
Rocha, B.M., Mendes, L., Chouvarda, I., Carvalho, P., Paiva, R.P.: Detection of cough and adventitious respiratory sounds in audio recordings by internal sound analysis. In: Maglaveras, N., Chouvarda, I., de Carvalho, P. (eds.) Precision Medicine Powered by pHealth and Connected Health. IP, vol. 66, pp. 51–55. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-7419-6_9
Serbes, G., Ulukaya, S., Kahya, Y.P.: An automated lung sound preprocessing and classification system based onspectral analysis methods. In: Maglaveras, N., Chouvarda, I., de Carvalho, P. (eds.) Precision Medicine Powered by pHealth and Connected Health. IP, vol. 66, pp. 45–49. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-7419-6_8
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)
Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, inception-resnet and the impact of residual connections on learning. In: AAAI, vol. 4, p. 12 (2017)
Palaz, D., Magimai-Doss, M., Collobert, R.: Analysis of CNN-based speech recognition system using raw speech as input. Technical report, Idiap (2015)
Weigend, A.S.: Time Series Prediction: Forecasting the Future and Understanding the Past. Routledge, New York (2018)
Rocha, B.M., et al.: A respiratory sound database for the development of automated classification. In: Maglaveras, N., Chouvarda, I., de Carvalho, P. (eds.) Precision Medicine Powered by pHealth and Connected Health. IP, vol. 66, pp. 33–37. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-7419-6_6
Jakovljević, N., Lončar-Turukalo, T.: Hidden Markov model based respiratory sound classification. In: Maglaveras, N., Chouvarda, I., de Carvalho, P. (eds.) Precision Medicine Powered by pHealth and Connected Health. IP, vol. 66, pp. 39–43. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-7419-6_7
Berouti, M., Schwartz, R., Makhoul, J.: Enhancement of speech corrupted by acoustic noise. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 1979, vol. 4, pp. 208–211. IEEE (1979)
Kochetov, K., Putin, E., Azizov, S., Skorobogatov, I., Filchenkov, A.: Wheeze detection using convolutional neural networks. In: Oliveira, E., Gama, J., Vale, Z., Lopes Cardoso, H. (eds.) EPIA 2017. LNCS (LNAI), vol. 10423, pp. 162–173. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-65340-2_14
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Cho, K., Van Merriënboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: encoder-decoder approaches. arXiv preprint arXiv:1409.1259 (2014)
Sundermeyer, M., Schlüter, R., Ney, H.: LSTM neural networks for language modeling. In: Thirteenth Annual Conference of the International Speech Communication Association (2012)
Graves, A., Mohamed, A., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE international conference on Acoustics, speech and signal processing (ICASSP), pp. 6645–6649. IEEE (2013)
Luong, M.-T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025 (2015)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. SSS. Springer, New York (2009). https://doi.org/10.1007/978-0-387-84858-7
Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13, 281–305 (2012)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Acknowledgements
This work was financially supported by the Government of the Russian Federation, Grant 08-08.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Kochetov, K., Putin, E., Balashov, M., Filchenkov, A., Shalyto, A. (2018). Noise Masking Recurrent Neural Network for Respiratory Sound Classification. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11141. Springer, Cham. https://doi.org/10.1007/978-3-030-01424-7_21
Download citation
DOI: https://doi.org/10.1007/978-3-030-01424-7_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01423-0
Online ISBN: 978-3-030-01424-7
eBook Packages: Computer ScienceComputer Science (R0)