{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T19:36:34Z","timestamp":1771702594217,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,6,17]],"date-time":"2021-06-17T00:00:00Z","timestamp":1623888000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31870938, 82070087"],"award-info":[{"award-number":["31870938, 82070087"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Zhejiang Province Key Research and Development Program","award":["2020C03073"],"award-info":[{"award-number":["2020C03073"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Mechanical ventilation is an essential life-support treatment for patients who cannot breathe independently. Patient\u2013ventilator asynchrony (PVA) occurs when ventilatory support does not match the needs of the patient and is associated with a series of adverse clinical outcomes. Deep learning methods have shown a strong discriminative ability for PVA detection, but they require a large number of annotated data for model training, which hampers their application to this task. We developed a transfer learning architecture based on pretrained convolutional neural networks (CNN) and used it for PVA recognition based on small datasets. The one-dimensional signal was converted to a two-dimensional image, and features were extracted by the CNN using pretrained weights for classification. A partial dropping cross-validation technique was developed to evaluate model performance on small datasets. When using large datasets, the performance of the proposed method was similar to that of non-transfer learning methods. However, when the amount of data was reduced to 1%, the accuracy of transfer learning was approximately 90%, whereas the accuracy of the non-transfer learning was less than 80%. The findings suggest that the proposed transfer learning method can obtain satisfactory accuracies for PVA detection when using small datasets. Such a method can promote the application of deep learning to detect more types of PVA under various ventilation modes.<\/jats:p>","DOI":"10.3390\/s21124149","type":"journal-article","created":{"date-parts":[[2021,6,17]],"date-time":"2021-06-17T04:15:46Z","timestamp":1623903346000},"page":"4149","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Identifying Patient\u2013Ventilator Asynchrony on a Small Dataset Using Image-Based Transfer Learning"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7145-6011","authenticated-orcid":false,"given":"Qing","family":"Pan","sequence":"first","affiliation":[{"name":"College of Information Engineering, Zhejiang University of Technology, Liuhe Rd. 288, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengzhe","family":"Jia","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Zhejiang University of Technology, Liuhe Rd. 288, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qijie","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Zhejiang University of Technology, Liuhe Rd. 288, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lingwei","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Zhejiang University of Technology, Liuhe Rd. 288, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Pan","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Zhejiang University of Technology, Liuhe Rd. 288, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Lu","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Zhejiang University of Technology, Liuhe Rd. 288, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2336-5323","authenticated-orcid":false,"given":"Zhongheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Qingchun East Rd. 3, Hangzhou 310016, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luping","family":"Fang","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Zhejiang University of Technology, Liuhe Rd. 288, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huiqing","family":"Ge","sequence":"additional","affiliation":[{"name":"Department of Respiratory Care, Regional Medical Center for National Institute of Respiratory Diseases, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Qingchun East Rd. 3, Hangzhou 310016, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1007\/s00134-015-3692-6","article-title":"Asynchronies during mechanical ventilation are associated with mortality","volume":"41","author":"Blanch","year":"2015","journal-title":"Intensive Care Med."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"De Haro, C., Sarlabous, L., Esperanza, J.A., Magrans, R., and Blanch, L. 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