{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T15:45:56Z","timestamp":1778168756569,"version":"3.51.4"},"reference-count":38,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,2,6]],"date-time":"2022-02-06T00:00:00Z","timestamp":1644105600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","award":["210510516"],"award-info":[{"award-number":["210510516"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia (FCT)","award":["SFRH\/BD\/135686\/2018 and 2020.04927.BD"],"award-info":[{"award-number":["SFRH\/BD\/135686\/2018 and 2020.04927.BD"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Respiratory diseases constitute one of the leading causes of death worldwide and directly affect the patient\u2019s quality of life. Early diagnosis and patient monitoring, which conventionally include lung auscultation, are essential for the efficient management of respiratory diseases. Manual lung sound interpretation is a subjective and time-consuming process that requires high medical expertise. The capabilities that deep learning offers could be exploited in order that robust lung sound classification models can be designed. In this paper, we propose a novel hybrid neural model that implements the focal loss (FL) function to deal with training data imbalance. Features initially extracted from short-time Fourier transform (STFT) spectrograms via a convolutional neural network (CNN) are given as input to a long short-term memory (LSTM) network that memorizes the temporal dependencies between data and classifies four types of lung sounds, including normal, crackles, wheezes, and both crackles and wheezes. The model was trained and tested on the ICBHI 2017 Respiratory Sound Database and achieved state-of-the-art results using three different data splitting strategies\u2014namely, sensitivity 47.37%, specificity 82.46%, score 64.92% and accuracy 73.69% for the official 60\/40 split, sensitivity 52.78%, specificity 84.26%, score 68.52% and accuracy 76.39% using interpatient 10-fold cross validation, and sensitivity 60.29% and accuracy 74.57% using leave-one-out cross validation.<\/jats:p>","DOI":"10.3390\/s22031232","type":"journal-article","created":{"date-parts":[[2022,2,6]],"date-time":"2022-02-06T20:40:18Z","timestamp":1644180018000},"page":"1232","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":169,"title":["Automated Lung Sound Classification Using a Hybrid CNN-LSTM Network and Focal Loss Function"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3371-569X","authenticated-orcid":false,"given":"Georgios","family":"Petmezas","sequence":"first","affiliation":[{"name":"Laboratory of Computing, Medical Informatics and Biomedical\u2014Imaging Technologies, Medical School, Aristotle University of Thessaloniki, GR 54124 Thessaloniki, Greece"}]},{"given":"Grigorios-Aris","family":"Cheimariotis","sequence":"additional","affiliation":[{"name":"Laboratory of Computing, Medical Informatics and Biomedical\u2014Imaging Technologies, Medical School, Aristotle University of Thessaloniki, GR 54124 Thessaloniki, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2682-5639","authenticated-orcid":false,"given":"Leandros","family":"Stefanopoulos","sequence":"additional","affiliation":[{"name":"Laboratory of Computing, Medical Informatics and Biomedical\u2014Imaging Technologies, Medical School, Aristotle University of Thessaloniki, GR 54124 Thessaloniki, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1643-667X","authenticated-orcid":false,"given":"Bruno","family":"Rocha","sequence":"additional","affiliation":[{"name":"Centre for Informatics and Systems, Department of Informatics Engineering, University of Coimbra, 3030-290 Coimbra, Portugal"}]},{"given":"Rui Pedro","family":"Paiva","sequence":"additional","affiliation":[{"name":"Centre for Informatics and Systems, Department of Informatics Engineering, University of Coimbra, 3030-290 Coimbra, Portugal"}]},{"given":"Aggelos K.","family":"Katsaggelos","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL 60208, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4919-0664","authenticated-orcid":false,"given":"Nicos","family":"Maglaveras","sequence":"additional","affiliation":[{"name":"Laboratory of Computing, Medical Informatics and Biomedical\u2014Imaging Technologies, Medical School, Aristotle University of Thessaloniki, GR 54124 Thessaloniki, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,6]]},"reference":[{"key":"ref_1","unstructured":"Forum of International Respiratory Societies (2017). 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