Key result
A photoplethysmogram-based Dilated Residual Neural Network estimated continuous respiratory rate with a mean absolute error of 3.1268 ± 0.6363 on the collected surgical ICU dataset.
Why the study?
Respiratory rate indicates patient prognosis but requires specific instrumentation or estimation, whereas PPG is already extensively monitored in ICUs and captures indirect intrathoracic pressure changes.
Does a PPG-based deep learning model accurately estimate continuous respiratory rate in surgical ICU patients?
Observational (n=100)
No
Does a PPG-based deep learning model accurately estimate continuous respiratory rate in surgical ICU patients?
Answers are grounded in this paper and cite their sources.
Deep learning models using PPG for continuous respiratory rate estimation show potential but require more diverse training data to improve accuracy in clinical settings.
The respiratory rate (RR) is a significant indicator to evaluate a patient's prognosis and status; however, it requires specific instrumentation or estimates from other monitored signals. A photoplethysmogram (PPG) is extensively used in clinical environments as well as in intensive care units (ICUs) to primarily monitor peripheral circulation while capturing indirect information about intrathoracic pressure changes. This study aims to apply and evaluate several deep learning models using a PPG for the continuous and accurate estimation of the RRs of patients. The dataset was collected twice for 2 min each in 100 patients aged 18 years and older from the surgical intensive care unit of a tertiary referral hospital. The BIDMC and CapnoBase public datasets were also analyzed. The collected dataset was preprocessed and split according to the 5-fold cross-validation. We used seven deep learning models, including our own Dilated Residual Neural Network, to check how accurately the RR estimates match the ground truth using the mean absolute error (MAE). As a result, when validated using the collected dataset, our model showed the best results with a 1.2628 ± 0.2697 MAE on BIDMC and RespNet and with a 3.1268 ± 0.6363 MAE on our dataset, respectively. In conclusion, RR estimation using PPG-derived models is still challenging and has many limitations. However, if there is an equal amount of data from various breathing groups to train, we expect that various models, including our Dilated ResNet model, which showed good results, can achieve better results than the current ones.
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Hwang et al. (2023) conducted an observational in Surgical intensive care unit patients (n=100). Photoplethysmogram-based deep learning models (Dilated Residual Neural Network) vs. Ground truth respiratory rate was evaluated on Mean absolute error (MAE) of respiratory rate estimates compared to ground truth (MAE 3.1268 ± 0.6363 on collected dataset; 1.2628 ± 0.2697 on BIDMC). A photoplethysmogram-based Dilated Residual Neural Network estimated continuous respiratory rate with a mean absolute error of 3.1268 ± 0.6363 on the collected surgical ICU dataset.
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