Responding to challenge call for machine learning model development in diagnosing respiratory disease sounds
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Abstract
The normal and abnormal sounds from the respiratory system shed great light on medical science by revealing the quality, diseases and changes in people’s lungs. In medicine, this easy and old method, realized by the stethoscope, facilitates the diagnosis of diseases by specialists. This manual method can sometimes lead to wrong decisions in terms of sound detection due to different audibility. Detailed sound analysis is crucial for accurately detecting lung diseases with high mortality rates. As technology advances, the development of automated approaches based on machine learning is of great interest as they provide modern and highly accurate analysis. In today’s most popular topic, i.e., the COVID-19 disaster, the conflict between early detection of respiratory disease and machine learning for sound signal processing is extremely important. In this study, a machine learning model was developed to detect respiratory system sounds such as sneezing/coughing in disease diagnosis. The automatic model and approach development of breath sounds, which carry valuable information, results in early diagnosis and treatment. A successful machine learning model was developed in this study, which was a strong response to the challenge called the “Pfizer digital medicine challenge” on the “OSFHOME” open access platform. In the database provided in this challenge, which consists of 3 parts, features that effectively showed coughing/sneezing sound analysis were extracted from training, testing and validating samples. Based on the Mel frequency cepstral coefficients (MFCC) feature extraction method, mathematical and statistical features were prepared. The sequential forward selection (SFS) feature selection method was used to select the relevant and dominant variables among the obtained features to represent the model fully and accurately. Three different classification techniques were considered for successful respiratory sound classification in the dataset containing more than 3800 sounds. Support vector machine (SVM) with radial basis function (RBF) kernels, decision tree and ensemble aggregation classification methods were used as classification techniques. In an attempt to classify coughing/sneezing sounds from other sounds, SVM with RBF kernels was achieved with 83% success.
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Accepted 2024-04-02
Published 2024-05-21
References
Abdulkareem, K.H., Mohammed, M.A., Salim, A., Arif, M., Geman, O., Gupta, D. and Khanna, A., 2021. Realizing an Effective COVID-19 Diagnosis System Based on Machine Learning and IoT in Smart Hospital Environment. IEEE Internet of Things Journal, 8(21), pp.15919–15928. Available from: https://doi.org/10.1109/JIOT.2021.3050775. DOI: https://doi.org/10.1109/JIOT.2021.3050775
Abeyratne, U., Swarnkar, V., Setyati, A. and Triasih, R., 2013. Cough Sound Analysis Can Rapidly Diagnose Childhood Pneumonia. Annals of Biomedical Engineering, 41, p.2448–2462. Available from: https://doi.org/10.1007/s10439-013-0836-0. DOI: https://doi.org/10.1007/s10439-013-0836-0
Alqudaihi, K.S., Aslam, N., Khan, I.U., Almuhaideb, A.M., Alsunaidi, S.J., Ibrahim, N.M.A.R., Alhaidari, F.A., Shaikh, F.S., Alsenbel, Y.M., Alalharith, D.M., Alharthi, H.M., Alghamdi, W.M. and Alshahrani, M.S., 2021. Cough Sound Detection and Diagnosis Using Artificial Intelligence Techniques: Challenges and Opportunities. IEEE Access, 9, pp.102327–102344. Available from: https://doi.org/10.1109/ACCESS.2021.3097559. DOI: https://doi.org/10.1109/ACCESS.2021.3097559
Alsunaidi, S.J., Almuhaideb, A.M., Ibrahim, N.M., Shaikh, F.S., Alqudaihi, K.S., Alhaidari, F.A., Khan, I.U., Aslam, N. and Alshahrani, M.S., 2021. Applications of Big Data Analytics to Control COVID-19 Pandemic. Sensors, 21(7). Available from: https://doi.org/10.3390/s21072282. DOI: https://doi.org/10.3390/s21072282
Amper-West, M., Saatchi, R., Barker, N. and Elphick, H., 2019. Respiratory sound analysis as a diagnosis tool for breathing disorders. The 32nd International Congress and Exhibition on Condition Monitoring and Diagnostic Engineering Management. Unpublished. Available from: http://shura.shu.ac.uk/24966/.
Andreu-Perez, J., Pérez-Espinosa, H., Timonet, E., Kiani, M., Girón-Pérez, M.I., Benitez-Trinidad, A.B., Jarchi, D., Rosales-Pérez, A., Gatzoulis, N., Reyes-Galaviz, O.F., Torres-García, A., Reyes-García, C.A., Ali, Z. and Rivas, F., 2022. A Generic Deep Learning Based Cough Analysis System From Clinically Validated Samples for Point-of-Need Covid-19 Test and Severity Levels. IEEE Transactions on Services Computing, 15(3), pp.1220–1232. Available from: https://doi.org/10.1109/TSC.2021.3061402. DOI: https://doi.org/10.1109/TSC.2021.3061402
Batra, M. and Agrawal, R., 2018. Comparative Analysis of Decision Tree Algorithms. In: B.K. Panigrahi, M.N. Hoda, V. Sharma and S. Goel, eds. Nature Inspired Computing. Singapore: Springer Singapore, pp.31–36. Available from: https://doi.org/10.1007/978-981-10-6747-1_4. DOI: https://doi.org/10.1007/978-981-10-6747-1_4
Bhateja, V., Taquee, A. and Sharma, D.K., 2019. Pre-Processing and Classification of Cough Sounds in Noisy Environment using SVM. 2019 4th International Conference on Information Systems and Computer Networks (ISCON). pp.822–826. Available from: https://doi.org/10.1109/ISCON47742.2019.9036277. DOI: https://doi.org/10.1109/ISCON47742.2019.9036277
Binnekamp, M., Stralen, K., Boer, L. and Houten, M., 2021. Typical RSV cough: myth or reality? A diagnostic accuracy study. European Journal of Pediatrics, 180, pp.57–62. Available from: https://doi.org/10.1007/s00431-020-03709-1. DOI: https://doi.org/10.1007/s00431-020-03709-1
developers scikit-learn, 2024. RBF SVM parameters — scikit-learn 1.4.1 documentation. Available from: https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html.
Doremalen, N. van, Bushmaker, T., Morris, D.H., Holbrook, M.G., Gamble, A., Williamson, B.N., Tamin, A., Harcourt, J.L., Thornburg, N.J., Gerber, S.I., Lloyd-Smith, J.O., Wit, E. de and Munster, V.J., 2020. Aerosol and Surface Stability of SARS-CoV-2 as Compared with SARS-CoV-1. New England Journal of Medicine, 382(16), pp.1564–1567. Available from: https://doi.org/10.1056/NEJMc2004973. DOI: https://doi.org/10.1056/NEJMc2004973
Fit binary decision tree for multiclass classification - MATLAB fitctree, 2024. Available from: https://www.mathworks.com/help/stats/fitctree.html.
Galar, M., Fernandez, A., Barrenechea, E., Bustince, H. and Herrera, F., 2012. A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(4), pp.463–484. Available from: https://doi.org/10.1109/TSMCC.2011.2161285. DOI: https://doi.org/10.1109/TSMCC.2011.2161285
Gemmeke, J.F., Ellis, D.P.W., Freedman, D., Jansen, A., Lawrence,W., Moore, R.C., Plakal, M. and Ritter, M., 2017. Audio Set: An ontology and human-labeled dataset for audio events. 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). pp.776–780. Available from: https://doi.org/10.1109/ICASSP.2017.7952261. DOI: https://doi.org/10.1109/ICASSP.2017.7952261
Gong, Y., Chung, Y.A. and Glass, J., 2021. AST: Audio Spectrogram Transformer. Proc. Interspeech 2021. pp.571–575. Available from: https://doi.org/10.21437/Interspeech.2021-698. DOI: https://doi.org/10.21437/Interspeech.2021-698
Google, 2024. AudioSet. Available from: https://research.google.com/audioset/.
Gudivada, V., Irfan, M., Fathi, E. and Rao, D., 2016. Cognitive Analytics: Going Beyond Big Data Analytics and Machine Learning. In: V.N. Gudivada, V.V. Raghavan, V. Govindaraju and C. Rao, eds. Cognitive Computing: Theory and Applications. Elsevier, Handbook of Statistics, vol. 35, chap. 5, pp.169–205. Available from: https://doi.org/10.1016/bs.host.2016.07.010. DOI: https://doi.org/10.1016/bs.host.2016.07.010
Gurung, A., Scrafford, C.G., Tielsch, J.M., Levine, O.S. and Checkley,W., 2011. Computerized lung sound analysis as diagnostic aid for the detection of abnormal lung sounds: A systematic review and meta-analysis. Respiratory Medicine, 105(9), pp.1396–1403. Available from: https://doi.org/10.1016/j.rmed.2011.05.007. DOI: https://doi.org/10.1016/j.rmed.2011.05.007
Hidayat, R., Bejo, A., Sumaryono, S. and Winursito, A., 2018. Denoising Speech for MFCC Feature Extraction Using Wavelet Transformation in Speech Recognition System. 2018 10th International Conference on Information Technology and Electrical Engineering (ICITEE). pp.280–284. Available from: https://doi.org/10.1109/ICITEED.2018.8534807. DOI: https://doi.org/10.1109/ICITEED.2018.8534807
Hoyos-Barceló, C., Monge-Álvarez, J., Pervez, Z., San-José-Revuelta, L.M. and Higuera, P.C. de-la, 2018. Efficient computation of image moments for robust cough detection using smartphones. Computers in Biology and Medicine, 100, pp.176–185. Available from: https://doi.org/10.1016/j.compbiomed.2018.07.003. DOI: https://doi.org/10.1016/j.compbiomed.2018.07.003
Imran, A., Posokhova, I., Qureshi, H.N., Masood, U., Riaz, M.S., Ali, K., John, C.N., Hussain, M.I. and Nabeel, M., 2020. AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app. Informatics in Medicine Unlocked, 20, p.100378. Available from: https://doi.org/10.1016/j.imu.2020.100378. DOI: https://doi.org/10.1016/j.imu.2020.100378
Infante, C., Chamberlain, D., Fletcher, R., Thorat, Y. and Kodgule, R., 2017. Use of cough sounds for diagnosis and screening of pulmonary disease. 2017 IEEE Global Humanitarian Technology Conference (GHTC). pp.1–10. Available from: https://doi.org/10.1109/GHTC.2017.8239338. DOI: https://doi.org/10.1109/GHTC.2017.8239338
Khomsay, S., Vanijjirattikhan, R. and Suwatthikul, J., 2019. Cough detection using PCA and Deep Learning. 2019 International Conference on Information and Communication Technology Convergence (ICTC). pp.101–106. Available from: https://doi.org/10.1109/ICTC46691.2019.8939769. DOI: https://doi.org/10.1109/ICTC46691.2019.8939769
Khriji, L., Ammari, A., Messaoud, S., Bouaafia, S., Maraoui, A. and Machhout, M., 2021. COVID-19 Recognition Based on Patient’s Coughing and Breathing Patterns Analysis: Deep Learning Approach. 2021 29th Conference of Open Innovations Association (FRUCT). pp.185–191. Available from: https://doi.org/10.23919/FRUCT52173.2021.9435454. DOI: https://doi.org/10.23919/FRUCT52173.2021.9435454
Knocikova, J., Korpas, J., Vrabec, M. and Javorka, M., 2008. Wavelet analysis of voluntary cough sound in patients with respiratory diseases. Journal of physiology and pharmacology, 59 Suppl 6, pp.331–40. Available from: https://www.jpp.krakow.pl/journal/archive/12_08_s6/pdf/331_12_08_s6_article.pdf.
Korpáš, J., Sadloňová, J. and Vrabec, M., 1996. Analysis of the Cough Sound: an Overview. Pulmonary Pharmacology, 9(5), pp.261–268. Available from: https://doi.org/10.1006/pulp.1996.0034. DOI: https://doi.org/10.1006/pulp.1996.0034
Kumar, A. and Ithapu, V.K., 2020. A sequential self teaching approach for improving generalization in sound event recognition. Proceedings of the 37th International Conference on Machine Learning. JMLR.org, ICML’20. Available from: https://proceedings.mlr.press/v119/kumar20a/kumar20a.pdf.
Laguarta, J., Hueto, F. and Subirana, B., 2020. COVID-19 Artificial Intelligence Diagnosis Using Only Cough Recordings. IEEE Open Journal of Engineering in Medicine and Biology, 1, pp.275–281. Available from: https://doi.org/10.1109/OJEMB.2020.3026928. DOI: https://doi.org/10.1109/OJEMB.2020.3026928
Malviya, A., Dixit, R., Shukla, A. and Kushwaha, N., 2023. Long Short-Term Memory-based Deep Learning Model for COVID-19 Detection using Coughing Sound. SN Computer Science, 4, pp.1–12. Available from: https://doi.org/10.1007/s42979-023-01934-7. DOI: https://doi.org/10.1007/s42979-023-01934-7
Matos, S., Birring, S., Pavord, I. and Evans, H., 2006. Detection of cough signals in continuous audio recordings using hidden Markov models. IEEE Transactions on Biomedical Engineering, 53(6), pp.1078–1083. Available from: https://doi.org/10.1109/TBME.2006.873548. DOI: https://doi.org/10.1109/TBME.2006.873548
Melek, M., 2021. Diagnosis of COVID-19 and Non-COVID-19 Patients by Classifying Only a Single Cough Sound. Neural Computing and Applications, 24, pp.17621–17632. Available from: https://doi.org/10.1007/s00521-021-06346-3. DOI: https://doi.org/10.1007/s00521-021-06346-3
Melek, N., 2022. Identifying COVID-19 by using spectral analysis of cough recordings: a distinctive classification study. Cognitive Neurodynamics, 16, pp.1–15. Available from: https://doi.org/10.1007/s11571-021-09695-w. DOI: https://doi.org/10.1007/s11571-021-09695-w
Nanni, L., Maguolo, G., Brahnam, S. and Paci, M., 2021. An Ensemble of Convolutional Neural Networks for Audio Classification. Applied Sciences, 11(13). Available from: https://doi.org/10.3390/app11135796. DOI: https://doi.org/10.3390/app11135796
Nemati, E., Rahman, M.M., Nathan, V., Vatanparvar, K. and Kuang, J., 2020. A Comprehensive Approach for Classification of the Cough Type. 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). pp.208–212. Available from: https://doi.org/10.1109/EMBC44109.2020.9175345. DOI: https://doi.org/10.1109/EMBC44109.2020.9175345
OSF | Dataset of sounds of symptoms associated with respiratory sickness Wiki, 2018. Available from: https://osf.io/tmkud/wiki/home/.
Piczak, K.J., 2015. ESC: Dataset for Environmental Sound Classification. Proceedings of the 23rd ACM International Conference on Multimedia. New York, NY, USA: Association for Computing Machinery, MM ’15, p.1015–1018. Available from: https://doi.org/10.1145/2733373.2806390. DOI: https://doi.org/10.1145/2733373.2806390
Polikar, R., 2006. Ensemble based systems in decision making. IEEE Circuits and Systems Magazine, 6(3), pp.21–45. Available from: https://doi.org/10.1109/MCAS.2006.1688199. DOI: https://doi.org/10.1109/MCAS.2006.1688199
Respiratory System: Functions, Facts, Organs & Anatomy, 2024. Available from: https://my.clevelandclinic.org/health/body/21205-respiratory-system.
Rudraraju, G., Palreddy, S., Mamidgi, B., Sripada, N.R., Sai, Y.P., Vodnala, N.K. and Haranath, S.P., 2020. Cough sound analysis and objective correlation with spirometry and clinical diagnosis. Informatics in Medicine Unlocked, 19, p.100319. Available from: https://doi.org/10.1016/j.imu.2020.100319. DOI: https://doi.org/10.1016/j.imu.2020.100319
Schapire, R., 1990. The Strength of Weak Learnability. Machine Learning, 5(2), pp.197–227. Available from: https://doi.org/10.1023/A:1022648800760. DOI: https://doi.org/10.1007/BF00116037
Sharma, G., Umapathy, K. and Krishnan, S., 2022. Audio texture analysis of COVID-19 cough, breath, and speech sounds. Biomedical Signal Processing and Control, 76, p.103703. Available from: https://doi.org/10.1016/j.bspc.2022.103703. DOI: https://doi.org/10.1016/j.bspc.2022.103703
Shastry, K.A., Sanjay, H. and Deexith, G., 2017. Quadratic-radial-basis-function-kernel for classifying multi-class agricultural datasets with continuous attributes. Applied Soft Computing, 58, pp.65–74. Available from: https://doi.org/10.1016/j.asoc.2017.04.049. DOI: https://doi.org/10.1016/j.asoc.2017.04.049
Shi, Y., Li, Y., Cai, M. and Zhang, X.D., 2019. A Lung Sound Category Recognition Method Based on Wavelet Decomposition and BP Neural Network. Int J Biol Sci, 15, pp.195–207. Available from: https://doi.org/10.7150/ijbs.29863. DOI: https://doi.org/10.7150/ijbs.29863
Shin, S.H., Hashimoto, T. and Hatano, S., 2009. Automatic Detection System for Cough Sounds as a Symptom of Abnormal Health Condition. IEEE Transactions on Information Technology in Biomedicine, 13(4), pp.486–493. Available from: https://doi.org/10.1109/TITB.2008.923771. DOI: https://doi.org/10.1109/TITB.2008.923771
Simou, N., Stefanakis, N. and Zervas, P., 2021. A Universal System for Cough Detection in Domestic Acoustic Environments. 2020 28th European Signal Processing Conference (EUSIPCO). pp.111–115. Available from: https://doi.org/10.23919/Eusipco47968.2020.9287659. DOI: https://doi.org/10.23919/Eusipco47968.2020.9287659
Smith, J., Ashurst, H., Jack, S., Woodcock, A. and Earis, J., 2006. The description of cough sounds by healthcare professionals. Cough (London, England), 2, p.1. Available from: https://doi.org/10.1186/1745-9974-2-1. DOI: https://doi.org/10.1186/1745-9974-2-1
Stanley, A. and Kucera, J., 2021. Smart Healthcare Devices and Applications, Machine Learning-based Automated Diagnostic Systems, and Real-Time Medical Data Analytics in COVID-19 Screening, Testing, and Treatment. American Journal of Medical Research, 8(2), p.105–117. Available from: https://doi.org/10.22381/ajmr8220218. DOI: https://doi.org/10.22381/ajmr8220218
Swarnkar, V., Abeyratne, U., Chang, A., Amrulloh, Y., Setyati, A. and Triasih, R., 2013. Automatic Identification of Wet and Dry Cough in Pediatric Patients with Respiratory Diseases. Annals of Biomedical Engineering, 41, p.1016–1028. Available from: https://doi.org/10.1007/s10439-013-0741-6. DOI: https://doi.org/10.1007/s10439-013-0741-6
Vatanparvar, K., Nemati, E., Nathan, V., Rahman, M.M. and Kuang, J., 2020. CoughMatch – Subject Verification Using Cough for Personal Passive Health Monitoring. 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). pp.5689–5695. Available from: https://doi.org/10.1109/EMBC44109.2020.9176835. DOI: https://doi.org/10.1109/EMBC44109.2020.9176835
Viitaniemi, V., Sjöberg, M., Koskela, M., Ishikawa, S. and Laaksonen, J., 2015. Chapter 12 - Advances in visual concept detection: Ten years of TRECVID. In: E. Bingham, S. Kaski, J. Laaksonen and J. Lampinen, eds. Advances in Independent Component Analysis and Learning Machines. Academic Press, pp.249–278. Available from: https://doi.org/10.1016/B978-0-12-802806-3.00012-9. DOI: https://doi.org/10.1016/B978-0-12-802806-3.00012-9
Wu, X., Kumar, V., Ross Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G.J., Ng, A., Liu, B., Yu, P.S., Zhou, Z.H., Steinbach, M., Hand, D.J. and Steinberg, D., 2007. Top 10 algorithms in data mining. Knowl. Inf. Syst., 14(1), p.1–37. Available from: https://doi.org/10.1007/s10115-007-0114-2. DOI: https://doi.org/10.1007/s10115-007-0114-2
Xin, X., Tu, Y., Stojanovic, V., Wang, H., Shi, K., He, S. and Pan, T., 2022. Online rein-forcement learning multiplayer non-zero sum games of continuous-time Markov jump linear systems. Applied Mathematics and Computation, 412, p.126537. Available from: https://doi.org/10.1016/j.amc.2021.126537. DOI: https://doi.org/10.1016/j.amc.2021.126537
Xu, Y., Cao, X. and Qiao, H., 2011. An Efficient Tree Classifier Ensemble-Based Approach for Pedestrian Detection. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 41(1), pp.107–117. Available from: https://doi.org/10.1109/TSMCB.2010.2046890. DOI: https://doi.org/10.1109/TSMCB.2010.2046890
Xu, Z., Li, X. and Stojanovic, V., 2021. Exponential stability of nonlinear state-dependent delayed impulsive systems with applications. Nonlinear Analysis: Hybrid Systems, 42, p.101088. Available from: https://doi.org/10.1016/j.nahs.2021.101088. DOI: https://doi.org/10.1016/j.nahs.2021.101088
Zhang, X., Wang, H., Stojanovic, V., Cheng, P., He, S., Luan, X. and Liu, F., 2022. Asynchronous Fault Detection for Interval Type-2 Fuzzy Nonhomogeneous Higher Level Markov Jump Systems With Uncertain Transition Probabilities. IEEE Transactions on Fuzzy Systems, 30(7), pp.2487–2499. Available from: https://doi.org/10.1109/TFUZZ.2021.3086224. DOI: https://doi.org/10.1109/TFUZZ.2021.3086224
Zhuang, Z., Tao, H., Chen, Y., Stojanovic, V. and Paszke,W., 2022. Iterative learning control for repetitive tasks with randomly varying trial lengths using successive projection. International Journal of Adaptive Control and Signal Processing, 36(5), pp.1196–1215. Available from: https://doi.org/10.1002/acs.3396. DOI: https://doi.org/10.1002/acs.3396