Ferkol, T., Schraufnagel, D.: The global burden of respiratory disease. Annals of the American Thoracic Society 11(3), 404–406 (2014)
Who, W.H.O.: Enfermedades no transmisibles. World Health Organization: WHO (2022)
Liu, Y., Lin, Y., Gao, S., Zhang, H., Wang, Z., Gao, Y., Chen, G.: Respiratory sounds feature learning with deep convolutional neural networks. In: 2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech), pp. 170–177 (2017). IEEE
Tan, K.S., Lim, R.L., Liu, J., Ong, H.H., Tan, V.J., Lim, H.F., Chung, K.F., Adcock, I.M., Chow, V.T., Wang, D.Y.: Respiratory viral infections in exacerbation of chronic airway inflammatory diseases: novel mechanisms and insights from the upper airway epithelium. Frontiers in Cell and Developmental Biology 8, 99 (2020)
Article PubMed PubMed Central Google Scholar
Pereira, D., Zubillaga, J., Alvarado, G., Bilbao, J., Pérez, M.: Prevalencia y factores de riesgo para EPOC en adultos indígenas y criollos de Maniapure, Venezuela: estudio piloto. RFM 43(1), 35–47 (2020)
Holland, A.E., Malaguti, C., Hoffman, M., Lahham, A., Burge, A.T., Dowman, L., May, A.K., Bondarenko, J., Graco, M., Tikellis, G., et al.: Home-based or remote exercise testing in chronic respiratory disease, during the covid-19 pandemic and beyond: a rapid review. Chronic respiratory disease 17, 1479973120952418 (2020)
Article PubMed PubMed Central Google Scholar
Chuang, M.-L., Lin, I.-F., Lee, C.-Y.: Clinical assessment tests in evaluating patients with chronic obstructive pulmonary disease: a cross-sectional study. Medicine 95(47) (2016)
Erickson, B.J., Korfiatis, P., Akkus, Z., Kline, T.L.: Machine learning for medical imaging. Radiographics 37(2), 505–515 (2017)
Das, N., Topalovic, M., Janssens, W.: Artificial intelligence in diagnosis of obstructive lung disease: current status and future potential. Current opinion in pulmonary medicine 24(2), 117–123 (2018)
Bellos, C.C., Papadopoulos, A., Rosso, R., Fotiadis, D.I.: Identification of copd patients’ health status using an intelligent system in the chronious wearable platform. IEEE Journal of Biomedical and Health Informatics 18(3), 731–738 (2014). https://doi.org/10.1109/JBHI.2013.2293172
Khatri, K.L., Tamil, L.S.: Early detection of peak demand days of chronic respiratory diseases emergency department visits using artificial neural networks. IEEE Journal of Biomedical and Health Informatics 22(1), 285–290 (2018). https://doi.org/10.1109/JBHI.2017.2698418
Badnjevic, A., Gurbeta, L., Custovic, E.: An expert diagnostic system to automatically identify asthma and chronic obstructive pulmonary disease in clinical settings. Scientific reports 8(1), 1–9 (2018)
Kaplan, A., Cao, H., FitzGerald, J.M., Iannotti, N., Yang, E., Kocks, J.W., Kostikas, K., Price, D., Reddel, H.K., Tsiligianni, I., et al.: Artificial intelligence/machine learning in respiratory medicine and potential role in asthma and copd diagnosis. The Journal of Allergy and Clinical Immunology: In Practice 9(6), 2255–2261 (2021)
Aykanat, M., KłŁłç, Ö., Kurt, B., Saryal, S.: Classification of lung sounds using convolutional neural networks. EURASIP Journal on Image and Video Processing 2017(1), 1–9 (2017)
Haider, N.S., Singh, B.K., Periyasamy, R., Behera, A.K.: Respiratory sound based classification of chronic obstructive pulmonary disease: a risk stratification approach in machine learning paradigm. Journal of medical systems 43, 1–13 (2019)
Chen, S., Huang, M., Peng, X., Yuan, Y., Huang, S., Ye, Y., Zhao, W., Li, B., Han, H., Yang, S., et al.: Lung sounds can be used as an indicator for assessing severity of chronic obstructive pulmonary disease at the initial diagnosis. Nan fang yi ke da xue xue bao= Journal of Southern Medical University 40(2), 177–182 (2020)
Chen, S., Huang, M., Peng, X., Yuan, Y., Huang, S., Ye, Y., Zhao, W., Li, B., Han, H., Yang, S., Cai, S., Zhao, H.: [lung sounds can be used as an indicator for assessing severity of chronic obstructive pulmonary disease at the initial diagnosis]. Nan fang yi ke da xue xue bao = Journal of Southern Medical University 40(2), 177–182 (2020). https://doi.org/10.12122/j.issn.1673-4254.2020.02.07
Mineshita, M., Kida, H., Handa, H., Nishine, H., Furuya, N., Nobuyama, S., Inoue, T., Matsuoka, S., Miyazawa, T.: The correlation between lung sound distribution and pulmonary function in copd patients. PloS one 9(9), 107506 (2014)
Xie, M., Liu, X., Cao, X., Guo, M., Li, X.: Trends in prevalence and incidence of chronic respiratory diseases from 1990 to 2017. Respiratory research 21(1), 1–13 (2020)
Windmon, A., Minakshi, M., Bharti, P., Chellappan, S., Johansson, M., Jenkins, B.A., Athilingam, P.R.: Tussiswatch: A smart-phone system to identify cough episodes as early symptoms of chronic obstructive pulmonary disease and congestive heart failure. IEEE journal of biomedical and health informatics 23(4), 1566–1573 (2018)
You, M., Wang, W., Li, Y., Liu, J., Xu, X., Qiu, Z.: Automatic cough detection from realistic audio recordings using C-BiLSTM with boundary regression. Biomed. Signal Process. Control 72, 103304 (2022). https://doi.org/10.1016/j.bspc.2021.103304 36569172
Mulimani, M.S., Rachh, R.R.: Edge computing in healthcare systems. In: Deep Learning and Edge Computing Solutions for High Performance Computing, pp. 63–100. Springer, (2021). https://doi.org/10.1007/978-3-030-60265-9_5
Ooko, S.O., Mukanyiligira, D., Munyampundu, J.P., Nsenga, J.: Edge ai-based respiratory disease recognition from exhaled breath signatures. In: 2021 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT), pp. 89–94. IEEE, (2021).https://doi.org/10.1109/JEEIT53412.2021.9634140
Global: number of smartphone users 2013-2028 \(\vert \) Statista. [Online; accessed 30. Aug. 2023] (2023). https://www.statista.com/forecasts/1143723/smartphone-users-in-the-world
Instituto Nacional de Estadística y Geografía (INEGI): Encuesta Nacional sobre Disponibilidad y Uso de Tecnologías de la Información en los Hogares (ENDUTIH) 2020. [Online; accessed 25. Aug. 2024] (2021). https://www.inegi.org.mx/contenidos/saladeprensa/boletines/2021/OtrTemEcon/ENDUTIH_2020.pdf
Rosenberg, S.: Smartphone Ownership Is Growing Rapidly Around the World, but Not Always Equally. Pew Research Center’s Global Attitudes Project (2020)
Bales, C., Nabeel, M., John, C.N., Masood, U., Qureshi, H.N., Farooq, H., Posokhova, I., Imran, A.: Can Machine Learning Be Used to Recognize and Diagnose Coughs? arXiv (2020). https://doi.org/10.1109/EHB50910.2020.9280115 2004.01495
Altan, G., Yayłk, A., Kutlu, Y.: Deep Learning with ConvNet Predicts Imagery Tasks Through EEG. Neural Process. Lett. 53(4), 2917–2932 (2021). https://doi.org/10.1007/s11063-021-10533-7
Binnekamp, M., Stralen, K.J., Boer, L.d., Houten, M.A.: Typical RSV cough: myth or reality? A diagnostic accuracy study. Eur. J. Pediatr. 180(1), 57–62 (2021). https://doi.org/10.1007/s00431-020-03709-1 32533258
Windmon, A., Minakshi, M., Chellappan, S., Athilingam, P., Johansson, M., Jenkins, B.A.: On detecting chronic obstructive pulmonary disease (copd) cough using audio signals recorded from smart-phones. In: HEALTHINF, pp. 329–338 (2018)
Kulkarni, S., Watanabe, H., Homma, F.: Self-Supervised Audio Encoder with Contrastive Pretraining for Respiratory Anomaly Detection. In: 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW), pp. 04–10. IEEE. https://doi.org/10.1109/ICASSPW59220.2023.10193030
Patel, P.J., Diwan, D., Patel, K.A., Ranga, S., Modi, N.J., Dumasia, S.: Multi Feature fusion for COPD Classification using Deep learning algorithms. J. Integr. Sci. Technol. 12(4), 780 (2024). https://doi.org/10.62110/sciencein.jist.2024.v12.780
Vatanparvar, K., Nemati, E., Nathan, V., Rahman, M.M., Kuang, J.: CoughMatch - Subject Verification Using Cough for Personal Passive Health Monitoring. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2020:5689-5695. (2020). https://doi.org/10.1109/EMBC44109.2020.9176835 33019267
Nestor, B., Hunter, J., Kainkaryam, R., Drysdale, E., Inglis, J.B., Shapiro, A., Nagaraj, S., Ghassemi, M., Foschini, L., Goldenberg, A.: Machine learning covid-19 detection from wearables. The Lancet Digital Health 5(4), 182–184 (2023)
Sreeram, A., Ravishankar, U., Sripada, N.R., Mamidgi, B.: Investigating the potential of mfcc features in classifying respiratory diseases. In: 2020 7th International Conference on Internet of Things: Systems, Management and Security (IOTSMS), pp. 1–7 (2020). IEEE
Rahmansyah, A., Andini, P., Dewi, O., Ningrum, T., Suryana, M.: Chronic obstructive pulmonary disease (copd) detection using cough sound analysis based on machine learning. In: Empowering Science and Mathematics for Global Competitiveness, pp. 589–594. CRC Press, (2019)
Ali, S.E., Khan, A.N., Zia, S.: Cough detection using mobile phone accelerometer and machine learning techniques. In: The Science Behind the COVID Pandemic and Healthcare Technology Solutions, pp. 405–431. Springer, (2022)
Sharan, P.: Automated discrimination of cough in audio recordings: A scoping review. Frontiers in Signal Processing 2, 759684 (2022)
Altan, G., Kutlu, Y., Pekmezci, A.Ö., Nural, S.: Deep learning with 3D-second order difference plot on respiratory sounds. Biomed. Signal Process. Control 45, 58–69 (2018). https://doi.org/10.1016/j.bspc.2018.05.014
Altan, G., Kutlu, Y., Allahverdi, N.: Deep Learning on Computerized Analysis of Chronic Obstructive Pulmonary Disease. IEEE J. Biomed. Health Inf. 24(5), 1344–1350 (2019). https://doi.org/10.1109/JBHI.2019.2931395
Altan, G., Kutlu, Y., Gökçen, A.: Chronic obstructive pulmonary disease severity analysis using deep learning onmulti-channel lung sounds. TÜBİTAK Academic Journals 28(5), 2979–2996 (2020). https://doi.org/10.3906/elk-2004-68
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., Rivas, F.: 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), 1220–1232 (2022). https://doi.org/10.1109/TSC.2021.3061402
Chorin, E., Padegimas, A., Havakuk, O., Birati, E.Y., Shacham, Y., Milman, A., Topaz, G., Flint, N., Keren, G., Rogowski, O.: Assessment of Respiratory Distress by the Roth Score. Clin. Cardiol. 39(11), 636–639 (2016). https://doi.org/10.1002/clc.22586 27701750
Melek, M.: Diagnosis of COVID-19 and non-COVID-19 patients by classifying only a single cough sound. Neural Comput. &. Applic. 33(24), 17621–17632 (2021). https://doi.org/10.1007/s00521-021-06346-3
Barry, S.J., Dane, A.D., Morice, A.H., Walmsley, A.D.: The automatic recognition and counting of cough. Cough 2(1), 1–9 (2006). https://doi.org/10.1186/1745-9974-2-8
Kahya, Y.P.: Breath Sound Recording. In: Breath Sounds, pp. 119–137. Springer, (2018). https://doi.org/10.1007/978-3-319-71824-8_8
Sainburg, T.: Timsainb/noisereduce: V1.0. https://doi.org/10.5281/zenodo.3243139.
Das, A.K., Naskar, R.: A deep learning model for depression detection based on MFCC and CNN generated spectrogram features. Biomed. Signal Process. Control 90, 105898 (2024). https://doi.org/10.1016/j.bspc.2023.105898
Wu, C.S., Kosuru, S., Tippareddy, S.: Bird Species Identification from Audio Data. In: 2023 IEEE Ninth International Conference on Big Data Computing Service and Applications (BigDataService), pp. 17–20. IEEE. https://doi.org/10.1109/BigDataService58306.2023.00015
Zhao, S., Li, S., Bao, Z., Jiang, G., Jiang, L., Zhang, L.: Deep Dense Autoencoder Using Modulation Spectrogram for Machine Unsupervised Anomaly Detection. In: The 2021 International Conference on Smart Technologies and Systems for Internet of Things, pp. 288–295. Springer, (2022). https://doi.org/10.1007/978-981-19-3632-6_36
Orlandic, L., Teijeiro, T., Atienza, D.: The COUGHVID crowdsourcing dataset, a corpus for the study of large-scale cough analysis algorithms. Sci. Data 8(156), 1–10 (2021). https://doi.org/10.1038/s41597-021-00937-4
Islam, R., Abdel-Raheem, E., Tarique, M.: A study of using cough sounds and deep neural networks for the early detection of Covid-19. Biomedical Engineering Advances 3, 100025 (2022). https://doi.org/10.1016/j.bea.2022.100025
Article PubMed PubMed Central Google Scholar
Sreeram, A.S.K., Ravishankar, U., Sripada, N.R., Mamidgi, B.: Investigating the potential of mfcc features in classifying respiratory diseases. In: 2020 7th International Conference on Internet of Things: Systems, Management and Security (IOTSMS), pp. 1–7 (2020). https://doi.org/10.1109/IOTSMS52051.2020.9340166
Ijaz, A., Nabeel, M., Masood, U., Mahmood, T., Hashmi, M.S., Posokhova, I., Rizwan, A., Imran, A.: Towards using cough for respiratory disease diagnosis by leveraging Artificial Intelligence: A survey. Inf. Med. Unlocked 29, 100832 (2022). https://doi.org/10.1016/j.imu.2021.100832
Shenfield, A., Howarth, M.: A Novel Deep Learning Model for the Detection and Identification of Rolling Element-Bearing Faults. Sensors (Basel, Switzerland) 20(18) (2020). https://doi.org/10.3390/s20185112
Lella, K.K., Pja, A.: Automatic COVID-19 disease diagnosis using 1D convolutional neural network and a
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