We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Machine Learning-Based Respiration Rate and Blood Oxygen Saturation Estimation Using Photoplethysmogram Signals.
- Authors
Shuzan, Md Nazmul Islam; Chowdhury, Moajjem Hossain; Chowdhury, Muhammad E. H.; Murugappan, Murugappan; Hoque Bhuiyan, Enamul; Arslane Ayari, Mohamed; Khandakar, Amith
- Abstract
The continuous monitoring of respiratory rate (RR) and oxygen saturation (SpO2) is crucial for patients with cardiac, pulmonary, and surgical conditions. RR and SpO2 are used to assess the effectiveness of lung medications and ventilator support. In recent studies, the use of a photoplethysmogram (PPG) has been recommended for evaluating RR and SpO2. This research presents a novel method of estimating RR and SpO2 using machine learning models that incorporate PPG signal features. A number of established methods are used to extract meaningful features from PPG. A feature selection approach was used to reduce the computational complexity and the possibility of overfitting. There were 19 models trained for both RR and SpO2 separately, from which the most appropriate regression model was selected. The Gaussian process regression model outperformed all the other models for both RR and SpO2 estimation. The mean absolute error (MAE) for RR was 0.89, while the root-mean-squared error (RMSE) was 1.41. For SpO2, the model had an RMSE of 0.98 and an MAE of 0.57. The proposed system is a state-of-the-art approach for estimating RR and SpO2 reliably from PPG. If RR and SpO2 can be consistently and effectively derived from the PPG signal, patients can monitor their RR and SpO2 at a cheaper cost and with less hassle.
- Subjects
OXYGEN in the blood; PHOTOPLETHYSMOGRAPHY; OXYGEN saturation; KRIGING; RESPIRATION; FEATURE selection; ARTIFICIAL respiration equipment
- Publication
Bioengineering (Basel), 2023, Vol 10, Issue 2, p167
- ISSN
2306-5354
- Publication type
Article
- DOI
10.3390/bioengineering10020167