COMPARISON OF ARTIFICIAL INTELLIGENCE PERFORMANCES OBTAINED IN DATASET CLASSIFICATIONS USING RESPIRATORY DATA

Öz

Diagnosis of disease with respiratory data is very important today as it was in the past. These diagnoses, which are mostly based on human experience, have begun to leave their place to machines with the development of technology. Especially with the emergence of the COVID-19 epidemic, studies on the ability of artificial intelligence to diagnose diseases by using respiratory data have increased. Sharing open-source data has paved the way for studies on this subject.

Artificial intelligence makes important contributions in many fields. In the field of health, significant accuracy results have been obtained in studies on respiratory sounds. In this article, a literat ure review on respiratory sounds and artificial intelligence achievements was made. 34 articles -that were selected from IEEE, Elsevier, Pubmed, and ScienceDirect digital databases and published after 2010- were used for comparisons. As keywords, "breathing sounds and", "respiratory sound classification", together with "artificial intelligence" and "machine learning" were chosen.

In this study, artificial intelligence methods used in 34 publications selected by literature review were compared in terms of the performances obtained in the training.

Anahtar Kelimeler

• Artificial intelligence
• machine learning
• respiratory sounds

Kaynakça

1. 1. Aykanat, M., Kılıç, Ö., Kurt, B., & Saryal, S. (2017). Classification of lung sounds using convolutional neural networks, Eurasip Journal on Image and Video Processing, 2017(1), 65.
2. 2. Acharya, J. & Basu, A. (2020). Deep Neural Network for Respiratory Sound Classification in Wearable Devices Enabled by Patient Specific Model Tuning, IEEE Transactions on Biomedical Circuits and Systems, 14(3), 535–544.
3. 3. Amoh, J. & Odame, K. (2016). Deep neural networks for identifying cough sounds, IEEETrans. Biomed. Circuits Syst., vol. 10, no. 5, pp. 1003–1011, Oct. 2016.
4. 4. Balli, O. & Kutlu, Y. (2020). Effect of Deep Learning Feature Inference Techniques on Respiratory Sounds Derin Öğrenme Öznitelik Çıkarma Tekniklerinin Solunum Sesleri Üzerindeki Etkisi, Journal of Intelligent Systems with Applications, 137–140.
5. 5. Bardou, D., Zhang, K. & Ahmad, S. M. (2018). Lung sounds classification using convolutional neural networks, Artificial Intelligence in Medicine, 88, 58–69.
6. 6. Basu, V. & Rana, S. (2020). Respiratory diseases recognition through respiratory sound with the help of deep neural network, 4th International Conference on Computational Intelligence and Networks, CINE 2020, 1–6.
7. 7. Bhowmik, R. T. & Most, S. P. (2022). A Personalized Respiratory Disease Exacerbation Prediction Technique Based on a Novel Spatio-Temporal Machine Learning Architecture and Local Environmental Sensor Networks, Electronics, 11(16), 2562.
8. 8. Brown, C., Chauhan, J., Grammenos, A., Han, J., Hasthanasombat, A., Spathis, D., Xia, T., Cicuta, P. & Mascolo, C. (2020). Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data, Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 3474–3484.

9. 9. Chamberlain, D., Kodgule, R., Ganelin, D., Miglani, V. & Fletcher, R. R. (2016 ). Application of semi- supervised deep learning to lung sound analysis, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), Orlando, FL, USA, 16–20 August 2016; pp. 804– 807.
10. 10. Chauhan, S., Wang, P., Sing Lim, C. & Anantharaman, V. (2008). A computer-aided MFCC-based HMM systemfor automatic auscultation, Computers in Biology and Medicine, 38(2), 221–233.
11. 11. Chen, H., Yuan, X., Pei, Z., Li, M., & Li, J. (2019a). Triple-Classification of Respiratory Sounds Using Optimized S-Transform and Deep Residual Networks, IEEE Access, 7, 32845–32852.
12. 12. Chen, H., Yuan, X., Li, J., Pei, Z. & Zheng, X. (2019b). Automatic Multi-Level In-Exhale Segmentation and Enhanced Generalized S-Transform for wheezing detection, Computer Methods Programs Biomed, 178, 163 – 173.
13. 13. Gronnesby, M., Solis, J.C.A., Holsbø, E., Melbye, H. & Bongo, L.A. (2017). Feature extraction for machine learning based crackle detection in lung sounds from a health survey, arXiv 2017, arXiv:1706.00005.
14. 14. Güler, H. C., Yıldız, ve O., Baysal, U., Cinyol, ve F. B., Koksal, D., Babaoğlu, E. & Sarınç Ulaşlı, S. (2020). Classification of Abnormal Respiratory Sounds Using Machine Learning Techniques, 2020 Medical Technologies Congress (TIPTEKNO), pp. 1-4.
15. 15. Haider, N. S. & Behera, A. K. (2022). Computerized lung sound based classification of asthma and chronic obstructive pulmonary disease (COPD), Biocybernetics and Biomedical Engineering, 42(1), 42–59.
16. 16. Hassan, A., Shahin, I. & Alsabek, M. B. (2020). COVID-19 Detection System using Recurrent Neural Networks, 2020 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI), pp. 1-5.
17. 17. Jakovljevi´c, N. & Lonˇcar-Turukalo, T. (2018). Hidden Markov Model Based Respiratory Sound Classification, In Precision Medicine Powered by pHealth and Connected Health. ICBHI 2017. IFMBE Proceedings; Maglaveras, N., Chouvarda, I., de Carvalho, P., Eds.; Springer: Singapore, 2018; Volume 66, pp. 39–43.
18. 18. Jayalakshmy, S., Priya, B. L. & Kavya, N. (2020). "CNN based Categorization of respiratory sounds using spectral descriptors". Proceedings of the 2020 IEEE International Conference on Communication, Computing and Industry 4.0, C2I4 2020.
19. 19. Laguarta, J., Hueto, F. & Subirana, B. (2020). COVID-19 Artificial Intelligence Diagnosis Using Only Cough Recordings. In IEEE Open Journal of Engineering in Medicine and Biology, vol. 1, pp. 275-281.
20. 20. LeCun, Y. Kavukcuoglu, K. & Farabet, C. (2010). Convolutional networks and applications in vision. Circuits and Systems (ISCAS), Proceedings of 2010 IEEE International Symposium, IEEE, pp. 253–256.
21. 21. Liu, R., Cai, S., Zhang, K. & Hu, N. (2019). Detection of Adventitious Respiratory Sounds based on Convolutional Neural Network, ICIIBMS 2019 - 4th International Conference on Intelligent Informatics and Biomedical Sciences, 298–303.
22. 22. Lozano, M., Fiz, J. A. & Jané, R. (2016). Automatic Differentiation of Normal and Continuous Adventitious Respiratory Sounds Using Ensemble Empirical Mode Decomposition and Instantaneous Frequ ency, IEEE J. Biomed. Health Inform, 20, 486–497.
23. 23. Ma, Y., Xu, X., Yu, Q., Zhang, Y., Li, Y., Zhao, J. & Wang, G. (2019). Lungbrn: A smart digital stethoscope for detecting respiratory disease using bi-resnet deep learning algorithm. BioCAS 2019 - Biomedical Circuits and Systems Conference, Proceedings, 1–4.
24. 24. Mendes, L., Vogiatzis, I.M. & Perantoni, et al. (2015). Detection of wheezes using their signature in the spectrogram space and musical features, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), Milan, Italy, 25–29 August 2015; pp. 5581–5584.
25. 25. Meng, F., Shi, Y., Wang, N., Cai, M. & Luo, Z. (2020). Detection of Respiratory Sounds Based on Wavelet Coefficients and Machine Learning, IEEE Acces s, 8, 155710–155720.
26. 26. Messner, E., Fediuk, M., Swatek, P., Scheidl, S., Smolle-Jüttner, F. M., Olschewski, H. & Pernkopf, F. (2020). Multi-channel lung sound classification with convolutional recurrent neural networks, Computers in Biology and Medicine, 122(May), 103831.
27. 27. Monaco, A., Amoroso, N., Bellantuono, L., Pantaleo, E., Tangaro, S. & Bellotti, R. (2020). Multi-time- scale features for accurate respiratory sound classification, Applied Sciences (Switzerland), 10(23), 1–17.
28. 28. Paraschiv, E. A. & Rotaru, C. M. (2020). Machine Learning Approaches based on Wearable Devices for Respiratory Diseases Diagnosis, 2020 International Conference on e-Health and Bioengineering (EHB), pp. 1-4.
29. 29. Pinho, C. Oliveira, A. Jácome, C. Rodrigues & J. Marques, A. (2015). Automatic crackle detection algorithm based on fractal dimension and box filtering, Procedia Comput. Sci., 2015, 64, 705–712.
30. 30. Riella, R., Nohama, P. & Maia, J. (2009). Method for automatic detection of wheezing in lung sounds, Braz. J. Med Biol. Res. 2009, 42, 674–684.
31. 31. Rizal, A., Hidayat, R. & Nugroho, H. A. (2017). Entropy measurement as features extraction in automatic lung sound classification, ICCREC 2017 - 2017 International Conference on Control, Electronics, Renewable Energy, and Communications, Proceedings, 2017-January, 93–97.
32. 32. Rocha, B. M., Pessoa, D., Marques, A., Carvalho, P. & Paiva, R. P. (2021 ). Automatic classification of adventitious respiratory sounds: A (un)solved problem? Sensors (Switzerland), 21(1), 1–19.
33. 33. Sen, I., Saraclar, M. & Kahya, Y. P. (2021). Differential diagnosis of asthma and COPD based on multivariate pulmonary sounds analysis. IEEE Trans Biomed Eng 2021; 68(5): 1601–10.
34. 34. Sreejyothi, S., Renjini, A., Raj, V., Swapna, M.N.S. & Sankararaman, S. I. (2021). Unwrapping the phase portrait features of adventitious crackle for auscultation and classification: a machine learning approach, Journal of Biological Physics, 47(2), 103–115.
35. 35. Srivastava, A., Jain, S., Miranda, R., Patil, S., Pandya, S. & Kotecha, K. (2021). Deep learning based respiratory sound analysis for detection of chronic obstructive pulmonary disease, PeerJ Computer Science 2021;7:e369.
36. 36. Stasiakiewicz P, Dobrowolski AP, Targowski T, Gałazzka- S´ widerek N, Sadura-Sieklucka T & Majka K, et al. (2021). Automatic classification of normal and sick patients with crackles using wavelet packet decomposition and support vector machine, Biomed Signal Process Control 2021;67:102521.