Identifying Patient–Ventilator Asynchrony on a Small Dataset Using Image-Based Transfer Learning

Sensors

Qing Pan¹  Mengzhe Jia¹  Qijie Liu¹  Lingwei Zhang¹  Jie Pan¹  Fei Lu¹  Luping Fang¹  Zhongheng Zhang²  Huiqing Ge³ 

[1] College of Information Engineering, Zhejiang University of Technology, Liuhe Rd. 288, Hangzhou 310023, China;Department of Emergency Medicine, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Qingchun East Rd. 3, Hangzhou 310016, China;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;

关键词: mechanical ventilation;    transfer learning;    deep learning;    patient–ventilator asynchrony;    convolutional neural network;   

DOI  :  10.3390/s21124149

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown