Detection of patient-ventilator asynchrony based on semi-supervised convolutional neural network

doi:10.3969/j.issn.1006-7795.2022.05.011

Abstract

Abstract: Objective To construct a patient-ventilator asynchrony (PVA) detection model based on semi-supervised convolutional neural networks (semi-CNN)and evaluate its diagnostic efficiency in pressure support ventilation (PSV) mode.Methods The mechanical ventilation data of 85 brain injury patients with PSV mode were analyzed retrospectively. Tracing of flow, and airway pressure combined with esophageal pressure data were used to identify manually the presence of PVA visually. Patient's future breathing observations were predicted by the Transformer-based time series prediction module. The output of the Transformer module was used as the input parameter of the semi-CNN to detect the occurrence of PVA. The accuracy, sensitivity, and specificity of the semi-CNN model and agreement between the semi-CNN model and human experts were validated in the test set. Results The initial training set included 513 normal breaths and 69 PVA breaths. After 500 epochs, the model converges.The test set included 48 normal breaths and 24 PVA breaths. In the test set, the accuracy,the sensitivity, and the specificity of the Transformer + semi-CNN model in identifying PVA are 0.92(0.83－0.97), 0.79(0.58 - 0.93), and 0.98(0.89 －1.00), respectively.There is substantial agreement between the semi-CNN and the human experts in identifying PVA (Kappa=0.80, 95%CI:0.65-0.95, P<0.01). Conclusion This study provided a PVA detecting method based on a semi-CNN algorithm with high accuracy and specificity. The proposed method showed a substantial agreement with human experts in identifying PVA and could be applied in real-time monitoring of PVA clinically.

Key words: mechanical ventilation, patient-ventilator asynchrony, deep learning, convolutional neural network, semi-supervised learning

CLC Number:

Zhou Yimin, Ning Zexing, Luo Xuying, He Xuan, Yang Yanlin, Chen Guangqiang, Li Ruirui, Zhou Jianxin. Detection of patient-ventilator asynchrony based on semi-supervised convolutional neural network[J]. Journal of Capital Medical University, 2022, 43(5): 734-739.

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URL: https://journal03.magtech.org.cn/Jweb_sdykdxxb/EN/10.3969/j.issn.1006-7795.2022.05.011

https://journal03.magtech.org.cn/Jweb_sdykdxxb/EN/Y2022/V43/I5/734

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