ArticleBioengineering (Basel, Switzerland)2023
Machine Learning-Based Respiration Rate and Blood Oxygen Saturation Estimation Using Photoplethysmogram Signals.
Article in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06544226 (A Novel Pilot Study Examining the Potential of Smartphones to Capture New Types of Data That Can Accurately Assess the Physiological and Psychological Health Status of an Individual With an Eating Disorder), which is not on this map. Cited by 15 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
A Novel Pilot Study Examining the Potential of Smartphones to Capture New Types of Data That Can Accurately Assess the Physiological and Psychological Health Status of an Individual With an Eating Disorder
Who cites it
15 citing papers in PubMed.
- AI-Driven and Automated Systems for Continuous Oxygen Saturation Monitoring in Long-Term Oxygen Therapy: Systematic Review.JMIRx med · 2026Review
- Artificial Intelligence and Digital Technologies in Prakriti Assessment: Toward Standardized Evidence-Based Ayurvedic Practice.Journal of Ayurveda and integrative medicine · 2026Review
- Photoplethysmography acceleration indices as digital biomarkers of cardiometabolic health.Frontiers in digital health · 2026Article
- Real-Time Subject-Specific Predictive Modeling of PPG Signals for Artifact-Resilient SpO2 Estimation Under Hypoxia.Sensors (Basel, Switzerland) · 2025Article
- SMART MAT: Fibre Optic Innovation for Bedside Monitoring and Validation of Continuous Vital Signs.Sensors (Basel, Switzerland) · 2025Article
- Oxygen Saturation Targeting in the Neonatal Intensive Care Unit.Journal of clinical medicine · 2025Review
- Integrated wearable PPG: a multi-vital sign monitoring based on group sparse mode decomposition framework in remote health care using PPG signal.Physical and engineering sciences in medicine · 2025Article
- Continuous respiratory rate monitoring through temporal fusion of ECG and PPG signals.PloS one · 2025Article
- Evaluating AI Methods for Pulse Oximetry: Performance, Clinical Accuracy, and Comprehensive Bias Analysis.Bioengineering (Basel, Switzerland) · 2024Article
- A Novel Respiratory Rate Estimation Algorithm from Photoplethysmogram Using Deep Learning Model.Diagnostics (Basel, Switzerland) · 2024Article
- Open-source software for respiratory rate estimation using single-lead electrocardiograms.Scientific reports · 2024Article
- Your smartphone could act as a pulse-oximeter and as a single-lead ECG.Scientific reports · 2023Article
- Estimating Blood Pressure during Exercise with a Cuffless Sphygmomanometer.Sensors (Basel, Switzerland) · 2023Article
- A Comparative Study on Predication of Appropriate Mechanical Ventilation Mode through Machine Learning Approach.Bioengineering (Basel, Switzerland) · 2023Article
- Parameter-Efficient Deep Learning Models for Vital Sign Estimation From PPG.Healthcare technology lettersArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.