Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.cmpb.2021.106601
Title: Quantification of respiratory effort magnitude in spontaneous breathing patients using Convolutional Autoencoders
Authors: Ang, Christopher Yew Shuen
Chiew, Yeong Shiong
Vu, Lien Hong
Cove, Matthew E 
Keywords: Spontaneous breathing
Machine learning
Convolutional Autoencoder (CAE)
Mechanical ventilation
Issue Date: 1-Mar-2022
Publisher: Elsevier BV
Citation: Ang, Christopher Yew Shuen, Chiew, Yeong Shiong, Vu, Lien Hong, Cove, Matthew E (2022-03-01). Quantification of respiratory effort magnitude in spontaneous breathing patients using Convolutional Autoencoders. Computer Methods and Programs in Biomedicine 215 : 106601-106601. ScholarBank@NUS Repository. https://doi.org/10.1016/j.cmpb.2021.106601
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
Abstract: Background: Spontaneous breathing (SB) effort during mechanical ventilation (MV) is an important metric of respiratory drive. However, SB effort varies due to a variety of factors, including evolving pathology and sedation levels. Therefore, assessment of SB efforts needs to be continuous and non-invasive. This is important to prevent both over- and under-assistance with MV. In this study, a machine learning model, Convolutional Autoencoder (CAE) is developed to quantify the magnitude of SB effort using only bedside MV airway pressure and flow waveform. Method: The CAE model was trained using 12,170,655 simulated SB flow and normal flow data (NB). The paired SB and NB flow data were simulated using a Gaussian Effort Model (GEM) with 5 basis functions. When the CAE model is given a SB flow input, it is capable of predicting a corresponding NB flow for the SB flow input. The magnitude of SB effort (SBEMag) is then quantified as the difference between the SB and NB flows. The CAE model was used to evaluate the SBEMag of 9 pressure control/ support datasets. Results were validated using a mean squared error (MSE) fitting between clinical and training SB flows. Results: The CAE model was able to produce NB flows from the clinical SB flows with the median SBEMag of the 9 datasets being 25.39% [IQR: 21.87–25.57%]. The absolute error in SBEMag using MSE validation yields a median of 4.77% [IQR: 3.77–8.56%] amongst the cohort. This shows the ability of the GEM to capture the intrinsic details present in SB flow waveforms. Analysis also shows both intra-patient and inter-patient variability in SBEMag. Conclusion: A Convolutional Autoencoder model was developed with simulated SB and NB flow data and is capable of quantifying the magnitude of patient spontaneous breathing effort. This provides potential application for real-time monitoring of patient respiratory drive for better management of patient-ventilator interaction.
Source Title: Computer Methods and Programs in Biomedicine
URI: https://scholarbank.nus.edu.sg/handle/10635/212801
ISSN: 01692607
DOI: 10.1016/j.cmpb.2021.106601
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
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