Using upsampling CONV-LSTM with metadata embedding for respiratory sound classification

Research Article
Open access

Using upsampling CONV-LSTM with metadata embedding for respiratory sound classification

Changhe Chen 1* , Rongbo Zhang 2
  • 1 University of Toronto    
  • 2 University of Toronto    
  • *corresponding author changhe.chen@mail.utoronto.ca
Published on 26 December 2023 | https://doi.org/10.54254/2753-8818/28/20230401
TNS Vol.28
ISSN (Print): 2753-8826
ISSN (Online): 2753-8818
ISBN (Print): 978-1-83558-261-9
ISBN (Online): 978-1-83558-262-6

Abstract

Respiratory diseases are one of the leading causes of death around the world and they severely affect patient quality of life. Auscultation is an essential method for diagnosing respiratory diseases, and it is low-cost and convenient. However, auscultation requires experts who are highly experienced. Medical trainees suffer from misdiagnosis inevitably. To address this issue, a novel machine learning model is proposed, which consists of upsampling convolutional neural network (CNN), a long short-term memory network (LSTM), and a fully connected network (FCNN) with embedding layers to classify respiratory sounds into seven categories: Normal (N), Rhonchi (R), Wheeze (W), Stridor (S), Coarse Crackle (CC), Fine Crackle (FC), Wheeze & Crackle (WC). The model is trained and evaluated on the SPRSound dataset and obtained the result on the test dataset with a sensitivity of 0.5716, specificity of 0.7882, average score of 0.6799, harmonic score of 0.6626, and total score of 0.6756.

Keywords:

respiratory sound classification, CNN, LSTM, upsampling, embedding, FCNN

Chen,C.;Zhang,R. (2023). Using upsampling CONV-LSTM with metadata embedding for respiratory sound classification. Theoretical and Natural Science,28,78-84.
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References

[1]. World Health Organization. (n.d.). Chronic obstructive pulmonary disease (COPD). World Health Organization. https://www.who.int/news-room/fact-sheets/detail/chronic-obstructive-pulmonary-disease-(copd)

[2]. Arts, L., Lim, E. H. T., van de Ven, P. M., Heunks, L., & Tuinman, P. R. (2020, April 30). The diagnostic accuracy of lung auscultation in adult patients with acute pulmonary pathologies: A meta-analysis. Scientific reports. https://www.ncbi.nlm.nih.gov/pmc/ articles/PMC7192898/#:~:text=Auscultation%20of%20the%20respiratory%20system,essential%20parts%20of%20clinical%20examination.

[3]. Kim, Y., Hyon, Y., Lee, S., Woo, S.-D., Ha, T., & Chung, C. (2022, March 31). The Coming Era of a new auscultation system for analyzing respiratory sounds - BMC pulmonary medicine. BioMed Central. https://bmcpulmmed.biomedcentral.com/articles/10.1186/ s12890-022-01896-1

[4]. MANGIONE, S., & NIEMAN, L. Z. (n.d.). American Journal of Respiratory and Critical Care Medicine. https://www.atsjournals.org/doi/10.1164/ajrccm.159.4.9806083

[5]. Kim, Y., Hyon, Y., Jung, S. S., Lee, S., Yoo, G., Chung, C., & Ha, T. (2021, August 25). Respiratory sound classification for Crackles, wheezes, and Rhonchi in the clinical field using Deep Learning. Nature News. https://www.nature.com/articles/s41598-021-96724-7

[6]. G. D. Clifford et al., "Classification of normal/abnormal heart sound recordings: The PhysioNet/Computing in Cardiology Challenge 2016," 2016 Computing in Cardiology Conference (CinC), Vancouver, BC, Canada, 2016, pp. 609-612.

[7]. A. Khamparia, D. Gupta, N. G. Nguyen, A. Khanna, B. Pandey and P. Tiwari. “Sound Classification Using Convolutional Neural Network and Tensor Deep Stacking Network," in IEEE Access, vol. 7, pp. 7717-7727, 2019, doi: 10.1109/ACCESS.2018.2888882.

[8]. Gimeno, P., Viñals, I., Ortega, A., Miguel, A., & Lleida, E. (2020, March 5). Multiclass audio segmentation based on recurrent neural networks for Broadcast Domain Data - EURASIP Journal on audio, speech, and music processing. SpringerOpen. https://asmp-eurasipjournals.springeropen.com/articles/10.1186/s13636-020-00172-6

[9]. Petmezas, G.; Cheimariotis, G.-A.; Stefanopoulos, L.; Rocha, B.; Paiva, R.P.; Katsaggelos, A.K.; Maglaveras, N. Automated Lung Sound Classification Using a Hybrid CNN-LSTM Network and Focal Loss Function. Sensors 2022, 22, 1232. https://doi.org/10. 3390/s22031232

[10]. Q. Zhang, et al. “SPRSound: Open-Source SJTU Paediatric Respiratory Sound Database”, IEEE Transactions on Biomedical Circuits and Systems (TBioCAS), pp. 1-13, 2022, early access.

[11]. Q. Zhang, et al. “Grand Challenge on Respiratory Sound Classification”, IEEE Biomedical Circuits and Systems Conference (BioCAS), 2022, pp. 1-5.

[12]. Recurrent neural networks cheatsheet star. CS 230 - Recurrent Neural Networks Cheatsheet. (n.d.). https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-recurrent-neural-networks

[13]. Hochreiter, S., & Schmidhuber, J. (1997, November 15). Long short-term memory. MIT Press. https://doi.org/10.1162/neco.1997.9.8.1735

[14]. Google. (n.d.). Embeddings | machine learning | google for developers. Google. https://developers.google.com/machine-learning/crash-course/embeddings/video-lecture#:~:text=An%20embedding%20is%20a%20relatively,like%20sparse%20vectors%20representing%20words.

Cite this article

Chen,C.;Zhang,R. (2023). Using upsampling CONV-LSTM with metadata embedding for respiratory sound classification. Theoretical and Natural Science,28,78-84.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Mathematical Physics and Computational Simulation

ISBN：978-1-83558-261-9(Print) / 978-1-83558-262-6(Online)
Editor：Roman Bauer
Conference website: https://www.confmpcs.org/
Conference date: 12 August 2023
Series: Theoretical and Natural Science
Volume number: Vol.28
ISSN：2753-8818(Print) / 2753-8826(Online)

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References

[1]. World Health Organization. (n.d.). Chronic obstructive pulmonary disease (COPD). World Health Organization. https://www.who.int/news-room/fact-sheets/detail/chronic-obstructive-pulmonary-disease-(copd)

[2]. Arts, L., Lim, E. H. T., van de Ven, P. M., Heunks, L., & Tuinman, P. R. (2020, April 30). The diagnostic accuracy of lung auscultation in adult patients with acute pulmonary pathologies: A meta-analysis. Scientific reports. https://www.ncbi.nlm.nih.gov/pmc/ articles/PMC7192898/#:~:text=Auscultation%20of%20the%20respiratory%20system,essential%20parts%20of%20clinical%20examination.

[3]. Kim, Y., Hyon, Y., Lee, S., Woo, S.-D., Ha, T., & Chung, C. (2022, March 31). The Coming Era of a new auscultation system for analyzing respiratory sounds - BMC pulmonary medicine. BioMed Central. https://bmcpulmmed.biomedcentral.com/articles/10.1186/ s12890-022-01896-1

[4]. MANGIONE, S., & NIEMAN, L. Z. (n.d.). American Journal of Respiratory and Critical Care Medicine. https://www.atsjournals.org/doi/10.1164/ajrccm.159.4.9806083

[5]. Kim, Y., Hyon, Y., Jung, S. S., Lee, S., Yoo, G., Chung, C., & Ha, T. (2021, August 25). Respiratory sound classification for Crackles, wheezes, and Rhonchi in the clinical field using Deep Learning. Nature News. https://www.nature.com/articles/s41598-021-96724-7

[6]. G. D. Clifford et al., "Classification of normal/abnormal heart sound recordings: The PhysioNet/Computing in Cardiology Challenge 2016," 2016 Computing in Cardiology Conference (CinC), Vancouver, BC, Canada, 2016, pp. 609-612.

[7]. A. Khamparia, D. Gupta, N. G. Nguyen, A. Khanna, B. Pandey and P. Tiwari. “Sound Classification Using Convolutional Neural Network and Tensor Deep Stacking Network," in IEEE Access, vol. 7, pp. 7717-7727, 2019, doi: 10.1109/ACCESS.2018.2888882.

[8]. Gimeno, P., Viñals, I., Ortega, A., Miguel, A., & Lleida, E. (2020, March 5). Multiclass audio segmentation based on recurrent neural networks for Broadcast Domain Data - EURASIP Journal on audio, speech, and music processing. SpringerOpen. https://asmp-eurasipjournals.springeropen.com/articles/10.1186/s13636-020-00172-6

[9]. Petmezas, G.; Cheimariotis, G.-A.; Stefanopoulos, L.; Rocha, B.; Paiva, R.P.; Katsaggelos, A.K.; Maglaveras, N. Automated Lung Sound Classification Using a Hybrid CNN-LSTM Network and Focal Loss Function. Sensors 2022, 22, 1232. https://doi.org/10. 3390/s22031232

[10]. Q. Zhang, et al. “SPRSound: Open-Source SJTU Paediatric Respiratory Sound Database”, IEEE Transactions on Biomedical Circuits and Systems (TBioCAS), pp. 1-13, 2022, early access.

[11]. Q. Zhang, et al. “Grand Challenge on Respiratory Sound Classification”, IEEE Biomedical Circuits and Systems Conference (BioCAS), 2022, pp. 1-5.

[12]. Recurrent neural networks cheatsheet star. CS 230 - Recurrent Neural Networks Cheatsheet. (n.d.). https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-recurrent-neural-networks

[13]. Hochreiter, S., & Schmidhuber, J. (1997, November 15). Long short-term memory. MIT Press. https://doi.org/10.1162/neco.1997.9.8.1735

[14]. Google. (n.d.). Embeddings | machine learning | google for developers. Google. https://developers.google.com/machine-learning/crash-course/embeddings/video-lecture#:~:text=An%20embedding%20is%20a%20relatively,like%20sparse%20vectors%20representing%20words.

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