A Novel Local Feature Generation Technique Based Sound Classification Method for Covid-19 Detection using Lung Breathing Sound

Öz

Lung breathing sounds have been used to diagnose many diseases, including Covid-19. Nowadays, Covid-19 has affected daily life worldwide, and it has caused a global pandemic. Generally, computer vision methods have been presented to classify healthy, pneumonia, and Covid-19. They achieved high classification rates on datasets with a limited number of classes without taking into consideration other lung diseases. Our main hypothesis is to detect Covid-19 automatically among other lung diseases by using lung breathing sounds. Therefore, a dataset of lung breathing sound with ten classes has been collected, and a novel lung sounds classification method has been proposed in this paper. This method presents a novel local feature generation technique, and Substitution Box (S-Box) of the present lightweight encryption method is utilized as a pattern. A novel nonlinear pattern is presented based on S-Box, named Present-SBox-Pat (present S-Box pattern). A new pooling-based transformation (maximum tent pooling (MaTP)) is proposed to generate high, middle, and low levels features. It is considered as a preprocessing method of this work. ReliefF and iterative neighbourhood component analysis (RFINCA) selector is used to select the most discriminative and informative features. Two shallow classifiers are used to obtain results. The proposed Present-SBox-Pat and MaTP feature generation network and RFINCA feature selector-based method achieved 95.43% classification accuracy using the SVM classifier. These results demonstrated the success of techniques in generating and selecting features that facilitate the task of classifiers.

Anahtar Kelimeler

• Lung breathing sound
• present SBox pattern
• Covid-19
• maximum tent pooling
• 2 levelled feature selector

Kaynakça

1. M. Aykanat, Ö. Kılıç, B. Kurt, S. Saryal, Classification of lung sounds using convolutional neural networks, EURASIP Journal on Image and Video Processing, 2017 (2017) 65.
2. U. Ozkaya, S. Ozturk, M. Barstugan, Coronavirus (COVID-19) Classification using Deep Features Fusion and Ranking Technique, arXiv preprint arXiv:2004.03698, (2020).
3. C. Huang, Y. Wang, X. Li, L. Ren, J. Zhao, Y. Hu, L. Zhang, G. Fan, J. Xu, X. Gu, Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China, The lancet, 395 (2020) 497-506.
4. L. Lan, D. Xu, G. Ye, C. Xia, S. Wang, Y. Li, H. Xu, Positive RT-PCR test results in patients recovered from COVID-19, Jama, 323 (2020) 1502-1503.
5. T. Ai, Z. Yang, H. Hou, C. Zhan, C. Chen, W. Lv, Q. Tao, Z. Sun, L. Xia, Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases, Radiology, (2020) 200642.
6. H. Pasterkamp, S.S. Kraman, G.R. Wodicka, Respiratory sounds: advances beyond the stethoscope, American journal of respiratory and critical care medicine, 156 (1997) 974-987.
7. J. Zhang, Y. Xie, Y. Li, C. Shen, Y. Xia, Covid-19 screening on chest x-ray images using deep learning based anomaly detection, arXiv preprint arXiv:2003.12338, (2020).
8. S. Amiriparian, S. Pugachevskiy, N. Cummins, S. Hantke, J. Pohjalainen, G. Keren, B. Schuller, CAST a database: Rapid targeted large-scale big data acquisition via small-world modelling of social media platforms, 2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII), IEEE2017, pp. 340-345.

9. M. Li, P. Lei, B. Zeng, Z. Li, P. Yu, B. Fan, C. Wang, Z. Li, J. Zhou, S. Hu, Coronavirus disease (COVID-19): spectrum of CT findings and temporal progression of the disease, Academic radiology, (2020).
10. A. Alimadadi, S. Aryal, I. Manandhar, P.B. Munroe, B. Joe, X. Cheng, Artificial intelligence and machine learning to fight COVID-19, American Physiological Society Bethesda, MD2020.
11. M. Barstugan, U. Ozkaya, S. Ozturk, Coronavirus (covid-19) classification using ct images by machine learning methods, arXiv preprint arXiv:2003.09424, (2020).
12. Z. Dokur, Respiratory sound classification by using an incremental supervised neural network, Pattern Analysis and Applications, 12 (2009) 309.
13. R.X.A. Pramono, S. Bowyer, E. Rodriguez-Villegas, Automatic adventitious respiratory sound analysis: A systematic review, PloS one, 12 (2017).
14. J. Schröder, J. Anemiiller, S. Goetze, Classification of human cough signals using spectro-temporal Gabor filterbank features, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE2016, pp. 6455-6459.
15. Z. Moussavi, A. Yadollahi, S. Camorlinga, Breathing sound analysis for detection of sleep apnea/popnea events, Google Patents2009.
16. M.S. Doyle, Analysis of lung sounds using neural networks, Vanderbilt University1994.
17. B. Sankur, Y.P. Kahya, E.Ç. Güler, T. Engin, Comparison of AR-based algorithms for respiratory sounds classification, Computers in Biology and Medicine, 24 (1994) 67-76.
18. B. Lei, S.A. Rahman, I. Song, Content-based classification of breath sound with enhanced features, Neurocomputing, 141 (2014) 139-147.
19. M. Shokrollahi, S. Saha, P. Hadi, F. Rudzicz, A. Yadollahi, Snoring sound classification from respiratory signal, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE2016, pp. 3215-3218.
20. S. Huq, Z. Moussavi, Acoustic breath-phase detection using tracheal breath sounds, Medical & biological engineering & computing, 50 (2012) 297-308.
21. J. Han, K. Qian, M. Song, Z. Yang, Z. Ren, S. Liu, J. Liu, H. Zheng, W. Ji, T. Koike, An Early Study on Intelligent Analysis of Speech under COVID-19: Severity, Sleep Quality, Fatigue, and Anxiety, arXiv preprint arXiv:2005.00096, (2020).
22. Z. Jiang, M. Hu, L. Fan, Y. Pan, W. Tang, G. Zhai, Y. Lu, Combining Visible Light and Infrared Imaging for Efficient Detection of Respiratory Infections such as COVID-19 on Portable Device, arXiv preprint arXiv:2004.06912, (2020).
23. H.S. Maghdid, K.Z. Ghafoor, A.S. Sadiq, K. Curran, K. Rabie, A novel AI-enabled framework to diagnose Coronavirus COVID 19 using smartphone embedded sensors: Design study, arXiv preprint arXiv:2003.07434, (2020).
24. A.A. Ardakani, A.R. Kanafi, U.R. Acharya, N. Khadem, A. Mohammadi, Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks, Computers in Biology and Medicine, (2020) 103795.
25. K. Li, Y. Fang, W. Li, C. Pan, P. Qin, Y. Zhong, X. Liu, M. Huang, Y. Liao, S. Li, CT image visual quantitative evaluation and clinical classification of coronavirus disease (COVID-19), European Radiology, (2020) 1-10.
26. F. Shi, L. Xia, F. Shan, D. Wu, Y. Wei, H. Yuan, H. Jiang, Y. Gao, H. Sui, D. Shen, Large-scale screening of covid-19 from community acquired pneumonia using infection size-aware classification, arXiv preprint arXiv:2003.09860, (2020).
27. A. Narin, C. Kaya, Z. Pamuk, Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks, arXiv preprint arXiv:2003.10849, (2020).
28. T. Ahonen, A. Hadid, M. Pietikainen, Face description with local binary patterns: Application to face recognition, IEEE transactions on pattern analysis and machine intelligence, 28 (2006) 2037-2041.
29. M. Elangovan, N. Sakthivel, S. Saravanamurugan, B.B. Nair, V. Sugumaran, Machine learning approach to the prediction of surface roughness using statistical features of vibration signal acquired in turning, Procedia Computer Science, 50 (2015) 282-288.
30. S.A. Dudani, The distance-weighted k-nearest-neighbour rule, IEEE Transactions on Systems, Man, and Cybernetics, (1976) 325-327.
31. J.A. Suykens, J. Vandewalle, Least squares support vector machine classifiers, Neural processing letters, 9 (1999) 293-300.
32. YouTube, Respiratory Covid-19 Sounds, (11.05.2020).
33. D. Scherer, A. Müller, S. Behnke, Evaluation of pooling operations in convolutional architectures for object recognition, International conference on artificial neural networks, Springer2010, pp. 92-101.
34. I. Belakhdar, W. Kaaniche, R. Djemal, B. Ouni, Single-channel-based automatic drowsiness detection architecture with a reduced number of EEG features, Microprocessors and Microsystems, 58 (2018) 13-23.
35. C. Qin, S. Song, G. Huang, L. Zhu, Unsupervised neighborhood component analysis for clustering, Neurocomputing, 168 (2015) 609-617.
36. A. Bogdanov, L.R. Knudsen, G. Leander, C. Paar, A. Poschmann, M.J. Robshaw, Y. Seurin, C. Vikkelsoe, PRESENT: An ultra-lightweight block cipher, International Workshop on Cryptographic Hardware and Embedded Systems, Springer2007, pp. 450-466.
37. V. Nandan, R.G.S. Rao, Minimization of digital logic gates and ultra-low power AES encryption core in 180CMOS technology, Microprocessors and Microsystems, 74 (2020) 103000.
38. T. Tuncer, F. Ertam, Neighborhood component analysis and reliefF based survival recognition methods for Hepatocellular carcinoma, Physica A: Statistical Mechanics and its Applications, 540 (2020) 123143.
39. U. Jain, K. Nathani, N. Ruban, A.N.J. Raj, Z. Zhuang, V.G. Mahesh, Cubic SVM Classifier Based Feature Extraction and Emotion Detection from Speech Signals, 2018 International Conference on Sensor Networks and Signal Processing (SNSP), IEEE2018, pp. 386-391.
40. D.J. Higham, N.J. Higham, MATLAB guide, SIAM2016.
41. A. Rosenberg, Classifying skewed data: Importance weighting to optimize average recall, Thirteenth Annual Conference of the International Speech Communication Association2012.
42. T. Tuncer, S. Dogan, P. Pławiak, U.R. Acharya, Automated arrhythmia detection using novel hexadecimal local pattern and multilevel wavelet transform with ECG signals, Knowledge-Based Systems, 186 (2019) 104923.
43. S.D. Kumar, S. Esakkirajan, S. Bama, B. Keerthiveena, A Microcontroller based Machine Vision Approach for Tomato Grading and Sorting using SVM Classifier, Microprocessors and Microsystems, (2020) 103090.