TY - JOUR
T1 - Quantification of respiratory effort magnitude in spontaneous breathing patients using convolutional autoencoders
AU - Ang, Christopher Yew Shuen
AU - Chiew, Yeong Shiong
AU - Vu, Lien Hong
AU - Cove, Matthew E
N1 - Funding Information:
The authors would like to thank the MedTech Centre of Research Expertise, University of Canterbury, New Zealand and Monash University Malaysia Advance Engineering Platform (AEP) for supporting of this research. We would also like to thank Dr NL Loo and Mr QA Ng for supporting this research. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2022/3
Y1 - 2022/3
N2 - 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.
AB - 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.
KW - Convolutional Autoencoder (CAE)
KW - Machine learning
KW - Mechanical ventilation
KW - Spontaneous breathing
UR - http://www.scopus.com/inward/record.url?scp=85121903635&partnerID=8YFLogxK
U2 - 10.1016/j.cmpb.2021.106601
DO - 10.1016/j.cmpb.2021.106601
M3 - Article
C2 - 34973606
AN - SCOPUS:85121903635
SN - 0169-2607
VL - 215
JO - Computer Methods and Programs in Biomedicine
JF - Computer Methods and Programs in Biomedicine
M1 - 106601
ER -