An interpretable 1D convolutional neural network for detecting patient-ventilator asynchrony in mechanical ventilation. (June 2021)

• Record Type: Journal Article
• Authors: Pan, Qing; Zhang, Lingwei; Jia, Mengzhe; Pan, Jie; Gong, Qiang; Lu, Yunfei; Zhang, Zhongheng; Ge, Huiqing; Fang, Luping
• Is Part Of: Computer methods and programs in biomedicine. Volume 204(2021)
• Publication Date: 2021-06
• Subjects: Mechanical ventilation -- Patient-ventilator asynchrony -- Deep learning -- Convolutional neural network -- Class activation map -- Interpretability; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2021.106057
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Detection of PVA in mechanical ventilation by 1D-CNN model. First effort to interpret deep learning based PVA classification results. Have a significant speed advantage over the LSTM model. Abstract: Background and Objective: Patient-ventilator asynchrony (PVA) is the result of a mismatch between the need of patients and the assistance provided by the ventilator during mechanical ventilation. Because the poor interaction between the patient and the ventilator is associated with inferior clinical outcomes, effort should be made to identify and correct their occurrence. Deep learning has shown promising ability in PVA detection; however, lack of network interpretability hampers its application in clinic. Methods: We proposed an interpretable one-dimensional convolutional neural network (1DCNN) to detect four most manifestation types of PVA (double triggering, ineffective efforts during expiration, premature cycling and delayed cycling) under pressure control ventilation mode and pressure support ventilation mode. A global average pooling (GAP) layer was incorporated with the 1DCNN model to highlight the sections of the respiratory waveform the model focused on when making a classification. Dilation convolution and batch normalization were introduced to the 1DCNN model for compensating the reduction of performance caused by the GAP layer. Results: The proposed interpretable 1DCNN exhibited comparable performance with the state-of-the-art deep learning model in PVA detection. The F1 scores for the detection of four types of PVA under pressure control ventilation and pressure support ventilation modes were greater than 0.96. The critical sections of the waveform used to detect PVA were highlighted, and found to be well consistent with the understanding of the respective type of PVA by experts. Conclusions: The findings suggest that the proposed 1DCNN can help detect PVA, and enhance the interpretability of the classification process to help clinicians better understand the results obtained from deep learning technology.