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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...

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Autores principales: Pan, Qing, Jia, Mengzhe, Liu, Qijie, Zhang, Lingwei, Pan, Jie, Lu, Fei, Zhang, Zhongheng, Fang, Luping, Ge, Huiqing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
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.
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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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