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Identifying Patient–Ventilator Asynchrony on a Small Dataset Using Image-Based Transfer Learning
Mechanical ventilation is an essential life-support treatment for patients who cannot breathe independently. Patient–ventilator 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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8235356/ https://www.ncbi.nlm.nih.gov/pubmed/34204238 http://dx.doi.org/10.3390/s21124149 |
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author | Pan, Qing Jia, Mengzhe Liu, Qijie Zhang, Lingwei Pan, Jie Lu, Fei Zhang, Zhongheng Fang, Luping Ge, Huiqing |
author_facet | Pan, Qing Jia, Mengzhe Liu, Qijie Zhang, Lingwei Pan, Jie Lu, Fei Zhang, Zhongheng Fang, Luping Ge, Huiqing |
author_sort | Pan, Qing |
collection | PubMed |
description | Mechanical ventilation is an essential life-support treatment for patients who cannot breathe independently. Patient–ventilator 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. |
format | Online Article Text |
id | pubmed-8235356 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-82353562021-06-27 Identifying Patient–Ventilator Asynchrony on a Small Dataset Using Image-Based Transfer Learning Pan, Qing Jia, Mengzhe Liu, Qijie Zhang, Lingwei Pan, Jie Lu, Fei Zhang, Zhongheng Fang, Luping Ge, Huiqing Sensors (Basel) Article Mechanical ventilation is an essential life-support treatment for patients who cannot breathe independently. Patient–ventilator 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. MDPI 2021-06-17 /pmc/articles/PMC8235356/ /pubmed/34204238 http://dx.doi.org/10.3390/s21124149 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Pan, Qing Jia, Mengzhe Liu, Qijie Zhang, Lingwei Pan, Jie Lu, Fei Zhang, Zhongheng Fang, Luping Ge, Huiqing Identifying Patient–Ventilator Asynchrony on a Small Dataset Using Image-Based Transfer Learning |
title | Identifying Patient–Ventilator Asynchrony on a Small Dataset Using Image-Based Transfer Learning |
title_full | Identifying Patient–Ventilator Asynchrony on a Small Dataset Using Image-Based Transfer Learning |
title_fullStr | Identifying Patient–Ventilator Asynchrony on a Small Dataset Using Image-Based Transfer Learning |
title_full_unstemmed | Identifying Patient–Ventilator Asynchrony on a Small Dataset Using Image-Based Transfer Learning |
title_short | Identifying Patient–Ventilator Asynchrony on a Small Dataset Using Image-Based Transfer Learning |
title_sort | identifying patient–ventilator asynchrony on a small dataset using image-based transfer learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8235356/ https://www.ncbi.nlm.nih.gov/pubmed/34204238 http://dx.doi.org/10.3390/s21124149 |
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