Detection of Wheeze Sounds in Respiratory Disorders: A Deep Learning Approach

Year 2024, , 20 - 32, 20.04.2024

Leen Hakkı , Görkem Serbes

https://doi.org/10.35860/iarej.1402462

Cited By: 1

Abstract

Respiratory disorders, including chronic obstructive pulmonary disease (COPD) and asthma, are major causes of death globally. Early diagnosis of these conditions is essential for effective treatment. Auscultation of the lungs is the traditional diagnostic method, which has drawbacks such as subjectivity and susceptibility to environmental interference. To overcome these limitations, this study presents a novel approach for wheeze detection using deep learning methods. This approach includes the usage of artificial data created by employing the open ICBHI dataset with the aim of improvement in generalization of learning models. Spectrograms that were obtained as the output of the Short-Time Fourier Transform analysis were employed in feature extraction. Two labeling approaches were used for model comparison. The first approach involved labeling after wheezing occurred, and the second approach assigned labels directly to the time steps where wheezing patterns are seen. Wheeze event detection was performed by constructing four RNN-based models (CNN-LSTM, CNN-GRU, CNN-BiLSTM, and CNN-BiGRU). It was observed that labeling wheeze events directly resulted in more precise detection, with exceptional performance exhibited by the CNN-BiLSTM model. This approach demonstrates the potential for improving respiratory disorders diagnosis and hence leading to improved patient care.

Keywords

Recurrent Neural Networks, Long Short-Term Memory, Short Time Fourier Transform, Wheezes, Respiratory Sounds

References

• 1. Cukic, V., Lovre, V., Dragisic, D., & Ustamujic, A. Asthma and chronic obstructive pulmonary disease (COPD) – differences and similarities. Materia Socio-Medica, 2012. 24(2): p. 100.
• 2. World Health Organization. (n.d.). Chronic obstructive pulmonary disease (COPD). World Health Organization. Retrieved [cited October 25, 2022]; Available from: https://www.who.int/news-room/fact-sheets/detail/chronic-obstructive-pulmonary-disease-(copd).
• 3. Liang, R., Feng, X., Shi, D., Yang, M., Yu, L., Liu, W., Zhou, M., Wang, X., Qiu, W., Fan, L., Wang, B., & Chen, W. The global burden of disease attributable to high fasting plasma glucose in 204 countries and territories, 1990-2019: An updated analysis for the Global Burden of Disease Study 2019. Diabetes/metabolism research and reviews, 2022. 38(8): e3572.
• 4. Göğüş, F. Z., Karlık, B., & Harman, G. Classification of asthmatic breath sounds by using wavelet transforms and neural networks. International Journal of Signal Processing Systems, 2014. 3(2): p. 106-111.
• 5. Güler, İ., Polat, H., & Ergün, U. Combining neural network and genetic algorithm for prediction of lung sounds. Journal of Medical Systems, 2005. 29: p. 217-231.
• 6. Yeginer, M., & Kahya, Y. P. Feature extraction for pulmonary crackle representation via wavelet networks. Computers in Biology and Medicine, 2009. 39(8): p. 713–721.
• 7. Reichert, S., Gass, R., Brandt, C., & Andrès, E. Analysis of respiratory sounds: State of the art. Clinical Medicine: Circulatory, Respiratory and Pulmonary Medicine, 2008. p. 45-58.
• 8. Pasterkamp, H., & Zielinski, D. The History and Physical Examination. Kendig’s Disorders of the Respiratory Tract in Children, 2019 (9th Edition). p. 2–25.
• 9. Sarkar, M., Madabhavi, I., Niranjan, N., & Dogra, M. Auscultation of the respiratory system. Annals of Thoracic Medicine, 2015. 10(3): p. 158-168.
• 10. Zaitseva, E. G., Chernetsky, M. V., & Shevel, N. A. About Possibility of Remote Diagnostics of the Respiratory System by Auscultation. Devices and Methods of Measurements, 2020. 11(2): p. 148-154.
• 11. Kim, Y., Hyon, Y., Jung, S. S., Lee, S., Yoo, G., Chung, C., & Ha, T. Respiratory sound classification for crackles, wheezes, and rhonchi in the clinical field using deep learning. Scientific Reports, 2021. 11(1): p. 1-11.
• 12. Hsu, F.-S., Huang, S.-R., Huang, C.-W., Huang, C.-J., Cheng, Y.-R., Chen, C.-C., Hsiao, J., Chen, C.-W., Chen, L.-C., Lai, Y.-C., Hsu, B.-F., Lin, N.-J., Tsai, W.-L., Wu, Y.-L., Tseng, T.-L., Tseng, C.-T., Chen, Y.-T., & Lai, F. Benchmarking of eight recurrent neural network variants for breath phase and adventitious sound detection on a self-developed open-access lung sound database—hf_lung_v1. PLOS ONE, 2021. 16(7): p. 1-26.
• 13. Rocha, B. M., Filos, D., Mendes, L., Serbes, G., Ulukaya, S., Kahya, Y. P., … de Carvalho, P. An open access database for the evaluation of respiratory sound classification algorithms. Physiological Measurement. 2019. 40: 035001 .
• 14. Jakovljević, N., & Lončar-Turukalo, T. Hidden Markov Model Based Respiratory Sound Classification. IFMBE Proceedings, 2017. 66: p. 39–43.
• 15. Chambres, G., Hanna, P., & Desainte-Catherine, M. Automatic Detection of Patient with Respiratory Diseases Using Lung Sound Analysis. 2018 International Conference on Content-Based Multimedia Indexing (CBMI). 2018. p. 1-6.
• 16. Kochetov, K., Putin, E., Balashov, M., Filchenkov, A., & Shalyto, A. 2018. Noise Masking Recurrent Neural Network for Respiratory Sound Classification. In Artificial Neural Networks and Machine Learning–ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018. Springer International Publishing. p. 208–217.
• 17. Ma, Y., Xu, X., Yu, Q., Zhang, Y., Li, Y., Zhao, J., & Wang, G. LungBRN: A Smart Digital Stethoscope for Detecting Respiratory Disease Using bi-ResNet Deep Learning Algorithm. 2019 IEEE Biomedical Circuits and Systems Conference (BioCAS), 2019. IEEE. p. 1-4.
• 18. Ngo, Pham, L., Nguyen, A., Phan, B., Tran, K., & Nguyen, T. (2021). Deep Learning Framework Applied For Predicting Anomaly of Respiratory Sounds. 2021 International Symposium on Electrical and Electronics Engineering (ISEE). IEEE. p. 42-47.
• 19. Acharya, J., & Basu, A. Deep Neural Network for Respiratory Sound Classification in Wearable Devices Enabled by Patient Specific Model Tuning. IEEE Transactions on Biomedical Circuits and Systems, 2020. 14(3): p. 535-544.
• 20. Serbes, G., Ulukaya, S., & Kahya, Y. P. An Automated Lung Sound Preprocessing and Classification System Based OnSpectral Analysis Methods. In Precision Medicine Powered by pHealth and Connected Health: ICBHI 2017, Thessaloniki, Greece, 18-21 November 2017. Springer Singapore. p. 45-49.
• 21. Demir, F., Sengur, A., & Bajaj, V. Convolutional neural networks based efficient approach for classification of lung diseases. Health Information Science and Systems, 2019. 8(1): 4.
• 22. Demir, F., Ismael, A. M., & Sengur, A. Classification of lung sounds with CNN model using parallel pooling structure. IEEE Access, 2020. 8: p. 105376-105383.
• 23. ER, M. B. Akciğer Seslerinin Derin öğrenme i̇le sınıflandırılması. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 2020. 8(4): p. 830–844. (In Turkish).
• 24. Asatani, N., Kamiya, T., Mabu, S., & Kido, S. Classification of respiratory sounds using improved convolutional recurrent neural network. Computers & Electrical Engineering, 2021. 94: 107367.
• 25. Fan, C.-Y., Liu, C.-P., Wang, K.-C., Jhan, J.-H., Wang, Y.-C. F., & Chen, J.-C. Face Feature Recovery via Temporal Fusion for Person Search. ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing. 2020, IEEE. p. 1893-1897.
• 26. Liu, R., Cai, S., Zhang, K., & Hu, N. Detection of Adventitious Respiratory Sounds based on Convolutional Neural Network. 2019 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS). 2019, IEEE. p. 298-303.
• 27. Perna, D., & Tagarelli, A. Deep Auscultation: Predicting Respiratory Anomalies and Diseases via Recurrent Neural Networks. 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS). 2019, IEEE. p. 50-55.
• 28. Zulfiqar, R., Majeed, F., Irfan, R., Rauf, H. T., Benkhelifa, E., & Belkacem, A. N. Abnormal respiratory sounds classification using deep CNN through Artificial Noise addition. Frontiers in Medicine, 2021. 8: 714811.
• 29. Nguyen, T., & Pernkopf, F. Lung sound classification using co-tuning and stochastic normalization. IEEE Transactions on Biomedical Engineering, 2022. 69(9): p. 2872–2882.
• 30. Saraiva, A., Santos, D., Francisco, A., Sousa, J., Ferreira, N., Soares, S., & Valente, A. Classification of respiratory sounds with convolutional neural network. Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies. 2020, Science and Technology Publications. p. 138-144.
• 31. Ntalampiras, S., & Potamitis, I. Automatic acoustic identification of respiratory diseases. Evolving Systems, 2020. 12(1): p. 69-77.
• 32. Krishnan, S. Advanced Analysis of Biomedical Signals. Biomedical Signal Analysis for Connected Healthcare, 2021: p. 157–222.
• 33. Li, L., Wu, Z., Xu, M., Meng, H. M., & Cai, L. Combining CNN and BLSTM to Extract Textual and Acoustic Features for Recognizing Stances in Mandarin Ideological Debate Competition. In Interspeech, 2016. p. 1392-1396.
• 34. Hakki, L., & Serbes, G. Wheeze Events Detection Using Convolutional Recurrent Neural Network. In 2023 Innovations in Intelligent Systems and Applications Conference (ASYU), Sivas, Turkiye, 2023. IEEE. p. 1-6.