Version 1
: Received: 25 April 2023 / Approved: 26 April 2023 / Online: 26 April 2023 (13:17:24 CEST)
How to cite:
Hwang, C.; Kim, Y.; Hyun, J.K.; Kim, J.; Lee, S.; Kim, C.; Rho, S.; Nam, J.; Kim, E.Y. Evaluation of a New Photoplethysmography Dataset for Real-time Respiratory Rate Prediction using a Deep Neural Network. Preprints2023, 2023040996. https://doi.org/10.20944/preprints202304.0996.v1
Hwang, C.; Kim, Y.; Hyun, J.K.; Kim, J.; Lee, S.; Kim, C.; Rho, S.; Nam, J.; Kim, E.Y. Evaluation of a New Photoplethysmography Dataset for Real-time Respiratory Rate Prediction using a Deep Neural Network. Preprints 2023, 2023040996. https://doi.org/10.20944/preprints202304.0996.v1
Hwang, C.; Kim, Y.; Hyun, J.K.; Kim, J.; Lee, S.; Kim, C.; Rho, S.; Nam, J.; Kim, E.Y. Evaluation of a New Photoplethysmography Dataset for Real-time Respiratory Rate Prediction using a Deep Neural Network. Preprints2023, 2023040996. https://doi.org/10.20944/preprints202304.0996.v1
APA Style
Hwang, C., Kim, Y., Hyun, J.K., Kim, J., Lee, S., Kim, C., Rho, S., Nam, J., & Kim, E.Y. (2023). Evaluation of a New Photoplethysmography Dataset for Real-time Respiratory Rate Prediction using a Deep Neural Network. Preprints. https://doi.org/10.20944/preprints202304.0996.v1
Chicago/Turabian Style
Hwang, C., Jungwoo Nam and Eun Young Kim. 2023 "Evaluation of a New Photoplethysmography Dataset for Real-time Respiratory Rate Prediction using a Deep Neural Network" Preprints. https://doi.org/10.20944/preprints202304.0996.v1
Abstract
Respiratory rate is an important biomarker that indicates changes in the clinical condition of critically ill patients, so a surveillance tool that can accurately monitor the changing respiratory rate in real time is needed. Through investigating various pairs of machine learning models, we proposed new machine learning model for real-time respiratory rate estimation using photoplethysmogram. New photoplethysmogram-driven respiratory rate dataset(StMary) was collected from surgical intensive care unit of a tertiary referral hospital, using photoplethysmogram signal collector. For 50patients and 50healthy volunteers, 2-minute photoplethysmogram was collected for each subject twice. To evaluate the respiratory rate of subject, it was inputted into the deep neural network model we built, and dataset was splitted into training, validation, testing dataset, then 4-fold cross validation was exploited. Our deep neural network model trained with StMary and two public datasets(BIDMC and CapnoBase) individually, or selectively merged dataset had shown a low error rate in respiration rate measurements. Our model trained with StMary showed low mean absolute error score(1.0273±0.8965), and trained with 3 datasets(CapnoBase, BIDMC and StMary) showed a lower error rate(1.7359±1.6724) than the model trained with CapnoBase and BIDMC(1.9480±1.6751). We could verify the performance of model evaluating respiratory rate from photoplethysmogram, and our dataset could contribute as the clinical research data that supports artificial intelligence models evaluating respiratory rate and surveillance tools to test whether their monitoring function works properly.
Keywords
Convolutional Neural Network; Deep Learning; Photoplethysmography; Respiratory Rate; Time Series
Subject
Biology and Life Sciences, Biology and Biotechnology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
