{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T17:07:20Z","timestamp":1774631240376,"version":"3.50.1"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,3,4]],"date-time":"2025-03-04T00:00:00Z","timestamp":1741046400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,3,4]],"date-time":"2025-03-04T00:00:00Z","timestamp":1741046400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100003141","name":"Consejo Nacional de Ciencia y Tecnolog\u00eda","doi-asserted-by":"publisher","award":["113535"],"award-info":[{"award-number":["113535"]}],"id":[{"id":"10.13039\/501100003141","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Med Syst"],"DOI":"10.1007\/s10916-025-02154-7","type":"journal-article","created":{"date-parts":[[2025,3,4]],"date-time":"2025-03-04T09:35:08Z","timestamp":1741080908000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Edge Computing System for Automatic Detection of Chronic Respiratory Diseases Using Audio Analysis"],"prefix":"10.1007","volume":"49","author":[{"given":"Jos\u00e9 Antonio","family":"Rivas-Navarrete","sequence":"first","affiliation":[]},{"given":"Humberto","family":"P\u00e9rez-Espinosa","sequence":"additional","affiliation":[]},{"given":"A. L.","family":"Padilla-Ortiz","sequence":"additional","affiliation":[]},{"given":"Ansel Y.","family":"Rodr\u00edguez-Gonz\u00e1lez","sequence":"additional","affiliation":[]},{"given":"Diana Cristina","family":"Garc\u00eda-Cambero","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,4]]},"reference":[{"issue":"3","key":"2154_CR1","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1513\/AnnalsATS.201311-405PS","volume":"11","author":"T Ferkol","year":"2014","unstructured":"Ferkol, T., Schraufnagel, D.: The global burden of respiratory disease. Annals of the American Thoracic Society 11(3), 404\u2013406 (2014)","journal-title":"Annals of the American Thoracic Society"},{"key":"2154_CR2","volume-title":"Enfermedades no transmisibles","author":"WHO Who","year":"2022","unstructured":"Who, W.H.O.: Enfermedades no transmisibles. World Health Organization: WHO (2022)"},{"key":"2154_CR3","doi-asserted-by":"crossref","unstructured":"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\u2013177 (2017). IEEE","DOI":"10.1109\/DASC-PICom-DataCom-CyberSciTec.2017.41"},{"key":"2154_CR4","doi-asserted-by":"publisher","first-page":"99","DOI":"10.3389\/fcell.2020.00099","volume":"8","author":"KS Tan","year":"2020","unstructured":"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)","journal-title":"Frontiers in Cell and Developmental Biology"},{"issue":"1","key":"2154_CR5","first-page":"35","volume":"43","author":"D Pereira","year":"2020","unstructured":"Pereira, D., Zubillaga, J., Alvarado, G., Bilbao, J., P\u00e9rez, M.: Prevalencia y factores de riesgo para EPOC en adultos ind\u00edgenas y criollos de Maniapure, Venezuela: estudio piloto. RFM 43(1), 35\u201347 (2020)","journal-title":"RFM"},{"key":"2154_CR6","doi-asserted-by":"publisher","first-page":"147997312095241","DOI":"10.1177\/1479973120952418","volume":"17","author":"AE Holland","year":"2020","unstructured":"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)","journal-title":"Chronic respiratory disease"},{"key":"2154_CR7","doi-asserted-by":"crossref","unstructured":"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)","DOI":"10.1097\/MD.0000000000005471"},{"issue":"2","key":"2154_CR8","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1148\/rg.2017160130","volume":"37","author":"BJ Erickson","year":"2017","unstructured":"Erickson, B.J., Korfiatis, P., Akkus, Z., Kline, T.L.: Machine learning for medical imaging. Radiographics 37(2), 505\u2013515 (2017)","journal-title":"Radiographics"},{"issue":"2","key":"2154_CR9","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1097\/MCP.0000000000000459","volume":"24","author":"N Das","year":"2018","unstructured":"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\u2013123 (2018)","journal-title":"Current opinion in pulmonary medicine"},{"issue":"3","key":"2154_CR10","doi-asserted-by":"publisher","first-page":"731","DOI":"10.1109\/JBHI.2013.2293172","volume":"18","author":"CC Bellos","year":"2014","unstructured":"Bellos, C.C., Papadopoulos, A., Rosso, R., Fotiadis, D.I.: Identification of copd patients\u2019 health status using an intelligent system in the chronious wearable platform. IEEE Journal of Biomedical and Health Informatics 18(3), 731\u2013738 (2014). https:\/\/doi.org\/10.1109\/JBHI.2013.2293172","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"issue":"1","key":"2154_CR11","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1109\/JBHI.2017.2698418","volume":"22","author":"KL Khatri","year":"2018","unstructured":"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\u2013290 (2018). https:\/\/doi.org\/10.1109\/JBHI.2017.2698418","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"issue":"1","key":"2154_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-018-30116-2","volume":"8","author":"A Badnjevic","year":"2018","unstructured":"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\u20139 (2018)","journal-title":"Scientific reports"},{"key":"2154_CR13","doi-asserted-by":"crossref","unstructured":"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\u20132261 (2021)","DOI":"10.1016\/j.jaip.2021.02.014"},{"issue":"1","key":"2154_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13640-017-0213-2","volume":"2017","author":"M Aykanat","year":"2017","unstructured":"Aykanat, M., K\u0142\u0141\u0142\u00e7, \u00d6., Kurt, B., Saryal, S.: Classification of lung sounds using convolutional neural networks. EURASIP Journal on Image and Video Processing 2017(1), 1\u20139 (2017)","journal-title":"EURASIP Journal on Image and Video Processing"},{"key":"2154_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10916-019-1388-0","volume":"43","author":"NS Haider","year":"2019","unstructured":"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\u201313 (2019)","journal-title":"Journal of medical systems"},{"key":"2154_CR16","unstructured":"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\u2013182 (2020)"},{"key":"2154_CR17","doi-asserted-by":"publisher","unstructured":"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\u2013182 (2020). https:\/\/doi.org\/10.12122\/j.issn.1673-4254.2020.02.07","DOI":"10.12122\/j.issn.1673-4254.2020.02.07"},{"issue":"9","key":"2154_CR18","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0107506","volume":"9","author":"M Mineshita","year":"2014","unstructured":"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)","journal-title":"PloS one"},{"issue":"1","key":"2154_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12931-020-1291-8","volume":"21","author":"M Xie","year":"2020","unstructured":"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\u201313 (2020)","journal-title":"Respiratory research"},{"issue":"4","key":"2154_CR20","doi-asserted-by":"publisher","first-page":"1566","DOI":"10.1109\/JBHI.2018.2872038","volume":"23","author":"A Windmon","year":"2018","unstructured":"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\u20131573 (2018)","journal-title":"IEEE journal of biomedical and health informatics"},{"key":"2154_CR21","doi-asserted-by":"publisher","unstructured":"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","DOI":"10.1016\/j.bspc.2021.103304"},{"key":"2154_CR22","doi-asserted-by":"publisher","unstructured":"Mulimani, M.S., Rachh, R.R.: Edge computing in healthcare systems. In: Deep Learning and Edge Computing Solutions for High Performance Computing, pp. 63\u2013100. Springer, (2021). https:\/\/doi.org\/10.1007\/978-3-030-60265-9_5","DOI":"10.1007\/978-3-030-60265-9_5"},{"key":"2154_CR23","doi-asserted-by":"publisher","unstructured":"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\u201394. IEEE, (2021).https:\/\/doi.org\/10.1109\/JEEIT53412.2021.9634140","DOI":"10.1109\/JEEIT53412.2021.9634140"},{"key":"2154_CR24","unstructured":"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"},{"key":"2154_CR25","unstructured":"Instituto Nacional de Estad\u00edstica y Geograf\u00eda (INEGI): Encuesta Nacional sobre Disponibilidad y Uso de Tecnolog\u00edas de la Informaci\u00f3n en los Hogares (ENDUTIH) 2020. [Online; accessed 25. Aug. 2024] (2021). https:\/\/www.inegi.org.mx\/contenidos\/saladeprensa\/boletines\/2021\/OtrTemEcon\/ENDUTIH_2020.pdf"},{"key":"2154_CR26","unstructured":"Rosenberg, S.: Smartphone Ownership Is Growing Rapidly Around the World, but Not Always Equally. Pew Research Center\u2019s Global Attitudes Project (2020)"},{"key":"2154_CR27","doi-asserted-by":"publisher","unstructured":"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","DOI":"10.1109\/EHB50910.2020.9280115"},{"issue":"4","key":"2154_CR28","doi-asserted-by":"publisher","first-page":"2917","DOI":"10.1007\/s11063-021-10533-7","volume":"53","author":"G Altan","year":"2021","unstructured":"Altan, G., Yay\u0142k, A., Kutlu, Y.: Deep Learning with ConvNet Predicts Imagery Tasks Through EEG. Neural Process. Lett. 53(4), 2917\u20132932 (2021). https:\/\/doi.org\/10.1007\/s11063-021-10533-7","journal-title":"Neural Process. Lett."},{"key":"2154_CR29","doi-asserted-by":"publisher","unstructured":"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\u201362 (2021). https:\/\/doi.org\/10.1007\/s00431-020-03709-1 32533258","DOI":"10.1007\/s00431-020-03709-1"},{"key":"2154_CR30","doi-asserted-by":"crossref","unstructured":"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\u2013338 (2018)","DOI":"10.5220\/0006549603290338"},{"key":"2154_CR31","doi-asserted-by":"publisher","unstructured":"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\u201310. IEEE. https:\/\/doi.org\/10.1109\/ICASSPW59220.2023.10193030","DOI":"10.1109\/ICASSPW59220.2023.10193030"},{"key":"2154_CR32","doi-asserted-by":"publisher","unstructured":"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","DOI":"10.62110\/sciencein.jist.2024.v12.780"},{"key":"2154_CR33","doi-asserted-by":"publisher","unstructured":"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","DOI":"10.1109\/EMBC44109.2020.9176835"},{"issue":"4","key":"2154_CR34","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1016\/S2589-7500(23)00045-6","volume":"5","author":"B Nestor","year":"2023","unstructured":"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\u2013184 (2023)","journal-title":"The Lancet Digital Health"},{"key":"2154_CR35","doi-asserted-by":"crossref","unstructured":"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\u20137 (2020). IEEE","DOI":"10.1109\/IOTSMS52051.2020.9340166"},{"key":"2154_CR36","doi-asserted-by":"crossref","unstructured":"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\u2013594. CRC Press, (2019)","DOI":"10.1201\/9780429461903-80"},{"key":"2154_CR37","doi-asserted-by":"crossref","unstructured":"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\u2013431. Springer, (2022)","DOI":"10.1007\/978-3-031-10031-4_19"},{"key":"2154_CR38","doi-asserted-by":"publisher","DOI":"10.3389\/frsip.2022.759684","volume":"2","author":"P Sharan","year":"2022","unstructured":"Sharan, P.: Automated discrimination of cough in audio recordings: A scoping review. Frontiers in Signal Processing 2, 759684 (2022)","journal-title":"Frontiers in Signal Processing"},{"key":"2154_CR39","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.bspc.2018.05.014","volume":"45","author":"G Altan","year":"2018","unstructured":"Altan, G., Kutlu, Y., Pekmezci, A.\u00d6., Nural, S.: Deep learning with 3D-second order difference plot on respiratory sounds. Biomed. Signal Process. Control 45, 58\u201369 (2018). https:\/\/doi.org\/10.1016\/j.bspc.2018.05.014","journal-title":"Biomed. Signal Process. Control"},{"issue":"5","key":"2154_CR40","doi-asserted-by":"publisher","first-page":"1344","DOI":"10.1109\/JBHI.2019.2931395","volume":"24","author":"G Altan","year":"2019","unstructured":"Altan, G., Kutlu, Y., Allahverdi, N.: Deep Learning on Computerized Analysis of Chronic Obstructive Pulmonary Disease. IEEE J. Biomed. Health Inf. 24(5), 1344\u20131350 (2019). https:\/\/doi.org\/10.1109\/JBHI.2019.2931395","journal-title":"IEEE J. Biomed. Health Inf."},{"issue":"5","key":"2154_CR41","doi-asserted-by":"publisher","first-page":"2979","DOI":"10.3906\/elk-2004-68","volume":"28","author":"G Altan","year":"2020","unstructured":"Altan, G., Kutlu, Y., G\u00f6k\u00e7en, A.: Chronic obstructive pulmonary disease severity analysis using deep learning onmulti-channel lung sounds. T\u00dcB\u0130TAK Academic Journals 28(5), 2979\u20132996 (2020). https:\/\/doi.org\/10.3906\/elk-2004-68","journal-title":"T\u00dcB\u0130TAK Academic Journals"},{"issue":"3","key":"2154_CR42","doi-asserted-by":"publisher","first-page":"1220","DOI":"10.1109\/TSC.2021.3061402","volume":"15","author":"J Andreu-Perez","year":"2022","unstructured":"Andreu-Perez, J., P\u00e9rez-Espinosa, H., Timonet, E., Kiani, M., Gir\u00f3n-P\u00e9rez, M.I., Benitez-Trinidad, A.B., Jarchi, D., Rosales-P\u00e9rez, A., Gatzoulis, N., Reyes-Galaviz, O.F., Torres-Garc\u00eda, A., Reyes-Garc\u00eda, 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\u20131232 (2022). https:\/\/doi.org\/10.1109\/TSC.2021.3061402","journal-title":"IEEE Transactions on Services Computing"},{"key":"2154_CR43","doi-asserted-by":"publisher","unstructured":"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\u2013639 (2016). https:\/\/doi.org\/10.1002\/clc.22586 27701750","DOI":"10.1002\/clc.22586"},{"key":"2154_CR44","doi-asserted-by":"publisher","unstructured":"Melek, M.: Diagnosis of COVID-19 and non-COVID-19 patients by classifying only a single cough sound. Neural Comput. &. Applic. 33(24), 17621\u201317632 (2021). https:\/\/doi.org\/10.1007\/s00521-021-06346-3","DOI":"10.1007\/s00521-021-06346-3"},{"issue":"1","key":"2154_CR45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1745-9974-2-8","volume":"2","author":"SJ Barry","year":"2006","unstructured":"Barry, S.J., Dane, A.D., Morice, A.H., Walmsley, A.D.: The automatic recognition and counting of cough. Cough 2(1), 1\u20139 (2006). https:\/\/doi.org\/10.1186\/1745-9974-2-8","journal-title":"Cough"},{"key":"2154_CR46","doi-asserted-by":"publisher","unstructured":"Kahya, Y.P.: Breath Sound Recording. In: Breath Sounds, pp. 119\u2013137. Springer, (2018). https:\/\/doi.org\/10.1007\/978-3-319-71824-8_8","DOI":"10.1007\/978-3-319-71824-8_8"},{"key":"2154_CR47","doi-asserted-by":"publisher","unstructured":"Sainburg, T.: Timsainb\/noisereduce: V1.0. https:\/\/doi.org\/10.5281\/zenodo.3243139.","DOI":"10.5281\/zenodo.3243139"},{"key":"2154_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105898","volume":"90","author":"AK Das","year":"2024","unstructured":"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","journal-title":"Biomed. Signal Process. Control"},{"key":"2154_CR49","doi-asserted-by":"publisher","unstructured":"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\u201320. IEEE. https:\/\/doi.org\/10.1109\/BigDataService58306.2023.00015","DOI":"10.1109\/BigDataService58306.2023.00015"},{"key":"2154_CR50","doi-asserted-by":"publisher","unstructured":"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\u2013295. Springer, (2022). https:\/\/doi.org\/10.1007\/978-981-19-3632-6_36","DOI":"10.1007\/978-981-19-3632-6_36"},{"issue":"156","key":"2154_CR51","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41597-021-00937-4","volume":"8","author":"L Orlandic","year":"2021","unstructured":"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\u201310 (2021). https:\/\/doi.org\/10.1038\/s41597-021-00937-4","journal-title":"Sci. Data"},{"key":"2154_CR52","doi-asserted-by":"publisher","DOI":"10.1016\/j.bea.2022.100025","volume":"3","author":"R Islam","year":"2022","unstructured":"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","journal-title":"Biomedical Engineering Advances"},{"key":"2154_CR53","doi-asserted-by":"publisher","unstructured":"Sreeram, A.S.K., Ravishankar, U., Sripada, N.R., Mamidgi, B.: Investigating the potential of mfcc features in classifying\u00a0respiratory diseases. In: 2020 7th International Conference on Internet of Things: Systems, Management and Security (IOTSMS), pp. 1\u20137 (2020). https:\/\/doi.org\/10.1109\/IOTSMS52051.2020.9340166","DOI":"10.1109\/IOTSMS52051.2020.9340166"},{"key":"2154_CR54","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2021.100832","volume":"29","author":"A Ijaz","year":"2022","unstructured":"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","journal-title":"Inf. Med. Unlocked"},{"key":"2154_CR55","doi-asserted-by":"publisher","unstructured":"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","DOI":"10.3390\/s20185112"},{"issue":"2","key":"2154_CR56","doi-asserted-by":"publisher","first-page":"240","DOI":"10.3934\/publichealth.2021019","volume":"8","author":"KK Lella","year":"2021","unstructured":"Lella, K.K., Pja, A.: Automatic COVID-19 disease diagnosis using 1D convolutional neural network and augmentation with human respiratory sound based on parameters: cough, breath, and voice. AIMS Public Health 8(2), 240 (2021). https:\/\/doi.org\/10.3934\/publichealth.2021019","journal-title":"AIMS Public Health"},{"issue":"5","key":"2154_CR57","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42979-020-00305-w","volume":"1","author":"MdM Islam","year":"2020","unstructured":"Islam, Md.M., Haque, Md.R., Iqbal, H., Hasan, Md.M., Hasan, M., Kabir, M.N.: Breast Cancer Prediction: A Comparative Study Using Machine Learning Techniques. SN Comput. Sci. 1(5), 1\u201314 (2020). https:\/\/doi.org\/10.1007\/s42979-020-00305-w","journal-title":"SN Comput. Sci."}],"container-title":["Journal of Medical Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10916-025-02154-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10916-025-02154-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10916-025-02154-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,4]],"date-time":"2025-03-04T09:35:23Z","timestamp":1741080923000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10916-025-02154-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,4]]},"references-count":57,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["2154"],"URL":"https:\/\/doi.org\/10.1007\/s10916-025-02154-7","relation":{},"ISSN":["1573-689X"],"issn-type":[{"value":"1573-689X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,4]]},"assertion":[{"value":"28 February 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 March 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The hospital approved the data collection protocol where it was applied, ensuring that all ethical considerations were taken into account, even though formal ethical approval was not required due to the non-invasive nature of the project. All participants gave their informed consent.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"33"}}