TY - JOUR
T1 - An adversarial learning approach to generate pressure support ventilation waveforms for asynchrony detection
AU - Hao, L.
AU - Bakkes, T.H.G.F.
AU - van Diepen, A.
AU - Chennakeshava, N.
AU - Bouwman, R.A.
AU - De Bie Dekker, A.J.R.
AU - Woerlee, P.H.
AU - Mojoli, F.
AU - Mischi, M.
AU - Shi, Y.
AU - Turco, S.
PY - 2024/6
Y1 - 2024/6
N2 - Background and objective: Mechanical ventilation is a life-saving treatment for critically-ill patients. During treatment, patient-ventilator asynchrony (PVA) can occur, which can lead to pulmonary damage, complications, and higher mortality. While traditional detection methods for PVAs rely on visual inspection by clinicians, in recent years, machine learning models are being developed to detect PVAs automatically. However, training these models requires large labeled datasets, which are difficult to obtain, as labeling is a labour-intensive and time-consuming task, requiring clinical expertise. Simulating the lung-ventilator interactions has been proposed to obtain large labeled datasets to train machine learning classifiers. However, the obtained data lacks the influence of different hardware, of servo-controlled algorithms, and different sources of noise. Here, we propose VentGAN, an adversarial learning approach to improve simulated data by learning the ventilator fingerprints from unlabeled clinical data. Methods: In VentGAN, the loss functions are designed to add characteristics of clinical waveforms to the generated results, while preserving the labels of the simulated waveforms. To validate VentGAN, we compare the performance for detection and classification of PVAs when training a previously developed machine learning algorithm with the original simulated data and with the data generated by VentGAN. Testing is performed on independent clinical data labeled by experts. The McNemar test is applied to evaluate statistical differences in the obtained classification accuracy. Results: VentGAN significantly improves the classification accuracy for late cycling, early cycling and normal breaths (p < 0.01); no significant difference in accuracy was observed for delayed inspirations (p = 0.2), while the accuracy decreased for ineffective efforts (p < 0.01). Conclusions: Generation of realistic synthetic data with labels by the proposed framework is feasible and represents a promising avenue for improving training of machine learning models.
AB - Background and objective: Mechanical ventilation is a life-saving treatment for critically-ill patients. During treatment, patient-ventilator asynchrony (PVA) can occur, which can lead to pulmonary damage, complications, and higher mortality. While traditional detection methods for PVAs rely on visual inspection by clinicians, in recent years, machine learning models are being developed to detect PVAs automatically. However, training these models requires large labeled datasets, which are difficult to obtain, as labeling is a labour-intensive and time-consuming task, requiring clinical expertise. Simulating the lung-ventilator interactions has been proposed to obtain large labeled datasets to train machine learning classifiers. However, the obtained data lacks the influence of different hardware, of servo-controlled algorithms, and different sources of noise. Here, we propose VentGAN, an adversarial learning approach to improve simulated data by learning the ventilator fingerprints from unlabeled clinical data. Methods: In VentGAN, the loss functions are designed to add characteristics of clinical waveforms to the generated results, while preserving the labels of the simulated waveforms. To validate VentGAN, we compare the performance for detection and classification of PVAs when training a previously developed machine learning algorithm with the original simulated data and with the data generated by VentGAN. Testing is performed on independent clinical data labeled by experts. The McNemar test is applied to evaluate statistical differences in the obtained classification accuracy. Results: VentGAN significantly improves the classification accuracy for late cycling, early cycling and normal breaths (p < 0.01); no significant difference in accuracy was observed for delayed inspirations (p = 0.2), while the accuracy decreased for ineffective efforts (p < 0.01). Conclusions: Generation of realistic synthetic data with labels by the proposed framework is feasible and represents a promising avenue for improving training of machine learning models.
KW - Data augmentation
KW - Generative adversarial networks
KW - Machine learning
KW - Mechanical ventilation
KW - Patient-ventilator asynchrony
UR - http://www.scopus.com/inward/record.url?scp=85190747890&partnerID=8YFLogxK
U2 - 10.1016/j.cmpb.2024.108175
DO - 10.1016/j.cmpb.2024.108175
M3 - Article
C2 - 38640840
AN - SCOPUS:85190747890
SN - 0169-2607
VL - 250
JO - Computer Methods and Programs in Biomedicine
JF - Computer Methods and Programs in Biomedicine
M1 - 108175
ER -