{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T12:27:43Z","timestamp":1789216063055,"version":"build-2803163510"},"reference-count":24,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T00:00:00Z","timestamp":1755907200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T00:00:00Z","timestamp":1755907200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Curr Allergy Asthma Rep"],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1007\/s11882-025-01213-w","type":"journal-article","created":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T06:28:43Z","timestamp":1755930523000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Investigation into the Classification of Cough Sounds for Early Asthma Screening"],"prefix":"10.1007","volume":"25","author":[{"given":"Yanming","family":"Huo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiajing","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huixian","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luyuan","family":"Jia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guo","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Congkang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xu","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shen-Ao","family":"Hao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongdong","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haotian","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,8,23]]},"reference":[{"key":"1213_CR1","doi-asserted-by":"publisher","first-page":"108843","DOI":"10.1016\/J.COMPBIOMED.2024.108843","volume":"179","author":"J Shen","year":"2024","unstructured":"Shen J, Zhang X, Lu Y, et al. Novel audio characteristic-dependent feature extraction and data augmentation methods for cough-based respiratory disease classification. Comput Biol Med. 2024;179:108843.\u00a0https:\/\/doi.org\/10.1016\/J.COMPBIOMED.2024.108843.","journal-title":"Comput Biol Med"},{"key":"1213_CR2","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1183\/23120541.00075-2022","volume":"8","author":"K Huang","year":"2022","unstructured":"Huang K, Gu X, Yang T, Xu J, Yang L, Zhao J, et al. Prevalence and burden of chronic cough in China: a national cross-sectional study. ERJ Open Res. 2022;8:75\u20132022. https:\/\/doi.org\/10.1183\/23120541.00075-2022.","journal-title":"ERJ Open Res"},{"key":"1213_CR3","doi-asserted-by":"publisher","unstructured":"Mehmet K, Datta PB, Tugce K, et al. GCLP: An automated asthma detection model based on global chaotic logistic pattern using cough sounds.Eng Appl Artif Intell 2024;127(PA). https:\/\/doi.org\/10.1016\/J.ENGAPPAI.2023.107184.","DOI":"10.1016\/J.ENGAPPAI.2023.107184"},{"key":"1213_CR4","doi-asserted-by":"publisher","first-page":"5431","DOI":"10.21437\/Interspeech.2023-428","volume":"2023","author":"A Roy","year":"2023","unstructured":"Roy A, Satija U. AsthmaSCELNet: A lightweight supervised contrastive embedding learning framework for asthma classification using lung sounds. Proceedings of Interspeech. 2023;2023:5431\u20135. https:\/\/doi.org\/10.21437\/Interspeech.2023-428.","journal-title":"Proceedings of Interspeech"},{"key":"1213_CR5","doi-asserted-by":"publisher","unstructured":"Roy A, Satija U. AsTFSONN: A unified framework based on time-frequency domain self-operational neural network for asthmatic lung sound classification. IEEE Int Symposium Med Meas Appl. (MeMeA). 2023;1\u20136. https:\/\/doi.org\/10.1109\/MeMeA57477.2023.10171911.","DOI":"10.1109\/MeMeA57477.2023.10171911"},{"key":"1213_CR6","doi-asserted-by":"publisher","unstructured":"Vodnala N, Yarlagadda PS, Bhuvana KS, Ch M, Sailaja K. Novel deep learning approaches to differentiate asthma and COPD based on cough sounds. In 2024 Parul International Conference on Engineering and Technology (PICET), Vadodara, India, 2024;1\u20134. https:\/\/doi.org\/10.1109\/PICET60765.2024.10716083.","DOI":"10.1109\/PICET60765.2024.10716083"},{"key":"1213_CR7","doi-asserted-by":"publisher","unstructured":"Varun BP, Jonnalagadda I, Vishnu Vardhan C, Srinivas M, Ullas S. A comparative analysis of machine learning classifiers for audio-based disease classification. In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), Kamand, India; 2024. pp. 1\u20137. https:\/\/doi.org\/10.1109\/ICCCNT61001.2024.10724370.","DOI":"10.1109\/ICCCNT61001.2024.10724370"},{"key":"1213_CR8","doi-asserted-by":"publisher","unstructured":"\u0106iri\u0107 D, Bogunovi\u0107 \u0110, Jankovi\u0107 M, Mileti\u0107 M. Breath sound acquisition in different environments.2024 11th International Conference on Electrical, Electronic and Computing Engineering (IcETRAN), Nis, Serbia; 2024. pp. 1\u20136. https:\/\/doi.org\/10.1109\/IcETRAN62308.2024.10735425.","DOI":"10.1109\/IcETRAN62308.2024.10735425"},{"key":"1213_CR9","doi-asserted-by":"publisher","unstructured":"Maithani V, Rajput U, Singh M, Bhakuni VS, Sharma K, Manwal M. Machine learning application in lung cancer prediction: Insights from random forest. 2024 IEEE 3rd World Conference on Applied Intelligence and Computing (AIC), Gwalior, India; 2024. pp. 173\u2013178. https:\/\/doi.org\/10.1109\/AIC61668.2024.10730856.","DOI":"10.1109\/AIC61668.2024.10730856"},{"key":"1213_CR10","doi-asserted-by":"publisher","unstructured":"B KS, S S. Early stage pneumonia disease detection using machine learning techniques. 2024 Second International Conference on Advances in Information Technology (ICAIT), Chikkamagaluru, Karnataka, India; 2024. pp. 1\u20134. https:\/\/doi.org\/10.1109\/ICAIT61638.2024.10690386.","DOI":"10.1109\/ICAIT61638.2024.10690386"},{"key":"1213_CR11","doi-asserted-by":"publisher","unstructured":"Kumar S, Shvetsov AV, Alsamhi SH. FuzzyGuard: A novel multimodal neuro-fuzzy framework for COPD early diagnosis. IEEE Internet of Things Journal. https:\/\/doi.org\/10.1109\/ICAIT61638.2024.10690386.","DOI":"10.1109\/ICAIT61638.2024.10690386"},{"key":"1213_CR12","doi-asserted-by":"publisher","unstructured":"Cao X. Research on audio classification of COVID-19 based on Deep learning. Lanzhou Jiaotong University, 2023. https:\/\/doi.org\/10.27205\/d.cnki.gltec.2023.000520.","DOI":"10.27205\/d.cnki.gltec.2023.000520"},{"key":"1213_CR13","doi-asserted-by":"publisher","first-page":"102486","DOI":"10.1016\/j.artmed.2022.102486","volume":"137","author":"F Manzella","year":"2023","unstructured":"Manzella F, Pagliarini G, Sciavicco G, Stan IE. The voice of COVID-19: breath and cough recording classification with temporal decision trees and random forests. Artif Intell Med. 2023;137:102486. https:\/\/doi.org\/10.1016\/j.artmed.2022.102486.","journal-title":"Artif Intell Med"},{"key":"1213_CR14","doi-asserted-by":"publisher","first-page":"100129","DOI":"10.1016\/j.ibmed.2023.100129","volume":"9","author":"SJ Kho","year":"2024","unstructured":"Kho SJ, Shiong BLC, Vong W-T, Boon LK, Pathmanathan MD, Rahman MABA, Xuan KP, Hanafi WNBW, Peariasamy KM, Hui PTH. Malaysian cough sound analysis and COVID-19 classification with deep learning. Intelligence-Based Medicine. 2024;9:100129. https:\/\/doi.org\/10.1016\/j.ibmed.2023.100129.","journal-title":"Intelligence-Based Medicine"},{"key":"1213_CR15","doi-asserted-by":"publisher","first-page":"104572","DOI":"10.1016\/j.compbiomed.2021.104572","volume":"135","author":"P Madhurananda","year":"2021","unstructured":"Madhurananda P, Klopper M, Warren R, Niesler T. COVID-19 cough classification using machine learning and global smartphone recordings. Comput Biol Med. 2021;135:104572. https:\/\/doi.org\/10.1016\/j.compbiomed.2021.104572.","journal-title":"Comput Biol Med"},{"key":"1213_CR16","doi-asserted-by":"publisher","unstructured":"Ghourabi M, Mourad-Chehade F, Soubra R, Chkeir A. Enhanced cough analysis using 1-dimensional CNN features for respiratory health diagnosis. IEEE International Symposium on Medical Measurements and Applications (MeMeA), Eindhoven, Netherlands; 2024. pp. 1\u20136. https:\/\/doi.org\/10.1109\/MeMeA60663.2024.10596786.","DOI":"10.1109\/MeMeA60663.2024.10596786"},{"key":"1213_CR17","doi-asserted-by":"publisher","first-page":"162697","DOI":"10.1109\/ACCESS.2024.3488797","volume":"12","author":"SU Amin","year":"2024","unstructured":"Amin SU, Abbas MS, Kim B, Jung Y, Seo S. Enhanced anomaly detection in pandemic surveillance videos: an attention approach with EfficientNet-B0 and CBAM integration. IEEE Access. 2024;12:162697\u2013712. https:\/\/doi.org\/10.1109\/ACCESS.2024.3488797.","journal-title":"IEEE Access"},{"key":"1213_CR18","doi-asserted-by":"publisher","unstructured":"Li Y. Research on cough event detection and classification based on Audio analysis technology. China Jiliang University. 2022. https:\/\/doi.org\/10.27819\/d.cnki.gzgjl.2022.000291.","DOI":"10.27819\/d.cnki.gzgjl.2022.000291"},{"key":"1213_CR19","doi-asserted-by":"publisher","unstructured":"Yunnan CAI. Research on cough sound recognition algorithm based on audio analysis. China Jiliang University. 2022. https:\/\/doi.org\/10.27819\/d.cnki.gzgjl.2022.000565.","DOI":"10.27819\/d.cnki.gzgjl.2022.000565"},{"key":"1213_CR20","doi-asserted-by":"publisher","unstructured":"Celesti A, et al. Leveraging audio biomarkers for enriching the tele-monitoring of patients. 2024 IEEE Symposium on Computers and Communications (ISCC), Paris, France; 2024. pp. 1\u20136. https:\/\/doi.org\/10.1109\/ISCC61673.2024.10733631.","DOI":"10.1109\/ISCC61673.2024.10733631"},{"key":"1213_CR21","doi-asserted-by":"publisher","unstructured":"Salamon J, Jacoby C, Bello JP. \u2018A dataset and taxonomy for urban sound research\u2019. In: Proceedings of the 22nd ACM international conference on Multimedia. 2014. pp. 1041\u20131044. https:\/\/doi.org\/10.1145\/2647868.2655045.","DOI":"10.1145\/2647868.2655045"},{"issue":"3","key":"1213_CR22","doi-asserted-by":"publisher","first-page":"3853","DOI":"10.32604\/cmc.2024.057538","volume":"81","author":"E Dazhi","year":"2024","unstructured":"Dazhi E, Liu J, Zhang M, Jiang H, Mao K. RE-SMOTE: a novel imbalanced sampling method based on SMOTE with radius estimation. Comput Mater Continua. 2024;81(3):3853\u201380. https:\/\/doi.org\/10.32604\/cmc.2024.057538.","journal-title":"Comput Mater Continua"},{"key":"1213_CR23","doi-asserted-by":"publisher","unstructured":"Hu J, Shen L, Sun G. \"Squeeze-and-Excitation Networks,\" 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA; 2018. pp. 7132-7141. https:\/\/doi.org\/10.1109\/CVPR.2018.00745.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"1213_CR24","doi-asserted-by":"publisher","unstructured":"Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift[J].JMLR.org, 2015. https:\/\/doi.org\/10.48550\/arXiv.1502.03167.","DOI":"10.48550\/arXiv.1502.03167"}],"container-title":["Current Allergy and Asthma Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11882-025-01213-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11882-025-01213-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11882-025-01213-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T06:28:46Z","timestamp":1755930526000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11882-025-01213-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,23]]},"references-count":24,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["1213"],"URL":"https:\/\/doi.org\/10.1007\/s11882-025-01213-w","relation":{},"ISSN":["1529-7322","1534-6315"],"issn-type":[{"value":"1529-7322","type":"print"},{"value":"1534-6315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,23]]},"assertion":[{"value":"8 July 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 August 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Some theses cited in this paper include DOIs assigned internally by China National Knowledge Infrastructure (CNKI). These DOIs may not be accessible through the international DOI resolver and are intended for internal CNKI platform retrieval only. Readers unable to access the full text via DOI can search for the thesis title and author information on the CNKI platform.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All reported studies\/experiments involving human or animal subjects performed by the authors were in accordance with the ethical standards of institutional and\/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human and Animal Rights"}},{"value":"During the preparation of this work, the authors used ChatGPT-4o (OpenAI) and DeepSeek to assist with reference formatting and automatic sorting, language polishing, grammar checking, and the improvement of expression and logical refinement in the English abstract and selected sections. After using these tools, the authors reviewed and edited the content and take full responsibility for the final published version.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"AI Declaration"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}],"article-number":"36"}}