Evaluation of the Photoplethysmogram-Based Deep Learning Model for Continuous Respiratory Rate Estimation in Surgical Intensive Care Unit.
Chi Shin HwangYong Hwan KimJung Kyun HyunJoon Hwang KimSeo Rak LeeChoong Min KimJung Woo NamEun Young KimPublished in: Bioengineering (Basel, Switzerland) (2023)
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.
Keyphrases
- intensive care unit
- deep learning
- end stage renal disease
- ejection fraction
- newly diagnosed
- chronic kidney disease
- healthcare
- peritoneal dialysis
- neural network
- prognostic factors
- mechanical ventilation
- machine learning
- convolutional neural network
- physical activity
- artificial intelligence
- multidrug resistant
- emergency department
- mental health
- patient reported outcomes
- electronic health record
- high resolution
- social media
- single cell
- respiratory tract