Identifying Patient–Ventilator Asynchrony on a Small Dataset Using Image-Based Transfer Learning
Abstract
:1. Introduction
2. Methods
2.1. Overview of the Method
2.2. Data Collection and Annotation
2.3. Preprocessing
- (1)
- Standardization. First, the P, F, and V of each breath were interpolated and resampled to a uniform length of 224. Then, each respiratory sequence was normalized according to Equation (1), where denotes the normalized data, denotes the original data, and and are the maximum and minimum values of the original data set, respectively.
- (2)
- Dimensional Transformation. Each 1D respiratory sequence was plotted in a 224 224 grayscale image, as shown in Figure 3.
- (3)
- Multichannel Fusion. The Pdiag, Fdiag and Vdiag were treated as three respective channels and combined into an RGB image.
2.4. Pretrained Models for Feature Extraction
2.5. Performance Evaluation
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Pretrained Model | No. of Respiratory Sequences |
---|---|
PVA–DT a | 20,398 |
PVA–IEE | 26,453 |
OTHER b | 470,770 |
Total c | 516,210 |
Pretrained Model | No. of Weights | No. of Features | Input Image Size |
---|---|---|---|
MobileNet | 3,230,914 | 1024 | 224 224 |
VGG-16 network | 14,721,602 | 1024 | 224 224 |
Inception–ResNetV2 | 54,339,810 | 1536 | 224 224 |
Type | Model Name | Feature Extractor | Classifiers |
---|---|---|---|
Pr_CNN_DC | Pr_Mobile_DC | Pretrained MobileNet extractor | Dense |
Pr_VGG_DC | Pretrained VGGNet extractor | Dense | |
Pr_InceRes _DC | Pretrained Inception–ResNetV2 extractor | Dense | |
Pr_CNN_SVM | Pr_Mobile_SVM | Pretrained MobileNet extractor | SVM |
Pr_VGG_SVM | Pretrained VGGNet extractor | SVM | |
Pr_InceRes _SVM | Pretrained Inception–ResNetV2 extractor | SVM | |
Rd_CNN_DC | Rd_Mobile_DC | MobileNet extractor with r.i.w. * | Dense |
Rd_VGG_DC | VGGNet extractor with r.i.w. | Dense | |
Rd_InceRes_DC | Inception–ResNetV2 extractor with r.i.w. | Dense | |
Rd_CNN_SVM | Rd_Mobile_ SVM | MobileNet extractor with r.i.w. | SVM |
Rd_VGG_ SVM | VGGNet extractor with r.i.w. | SVM | |
Rd_InceRes_ SVM | Inception–ResNetV2 extractor with r.i.w. | SVM | |
Manual_SVM | Manual feature design | SVM | |
LSTM | 2-layer LSTM network | Dense |
ACC | SPE | SEN | F1 | |
---|---|---|---|---|
Manual_SVM | 0.9210.004 | 0.9460.003 | 0.8950.005 | 0.9180.004 |
Pr_CNN_SVM | 0.9660.005 | 0.9580.007 | 0.9730.009 | 0.9650.005 |
Rd_CNN_SVM | 0.9490.012 | 0.9430.009 | 0.9570.018 | 0.9490.013 |
ACC | SPE | SEN | F1 | |
---|---|---|---|---|
Manual_SVM | ||||
Pr_CNN_SVM | ||||
Rd_CNN_SVM |
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Pan, Q.; Jia, M.; Liu, Q.; Zhang, L.; Pan, J.; Lu, F.; Zhang, Z.; Fang, L.; Ge, H. Identifying Patient–Ventilator Asynchrony on a Small Dataset Using Image-Based Transfer Learning. Sensors 2021, 21, 4149. https://doi.org/10.3390/s21124149
Pan Q, Jia M, Liu Q, Zhang L, Pan J, Lu F, Zhang Z, Fang L, Ge H. Identifying Patient–Ventilator Asynchrony on a Small Dataset Using Image-Based Transfer Learning. Sensors. 2021; 21(12):4149. https://doi.org/10.3390/s21124149
Chicago/Turabian StylePan, Qing, Mengzhe Jia, Qijie Liu, Lingwei Zhang, Jie Pan, Fei Lu, Zhongheng Zhang, Luping Fang, and Huiqing Ge. 2021. "Identifying Patient–Ventilator Asynchrony on a Small Dataset Using Image-Based Transfer Learning" Sensors 21, no. 12: 4149. https://doi.org/10.3390/s21124149
APA StylePan, Q., Jia, M., Liu, Q., Zhang, L., Pan, J., Lu, F., Zhang, Z., Fang, L., & Ge, H. (2021). Identifying Patient–Ventilator Asynchrony on a Small Dataset Using Image-Based Transfer Learning. Sensors, 21(12), 4149. https://doi.org/10.3390/s21124149