ArticleDiagnostics (Basel, Switzerland)2024
A Novel Respiratory Rate Estimation Algorithm from Photoplethysmogram Using Deep Learning Model.
Article in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Networks of respiratory-muscular coupling in exercise and fatigue in young adults.Physiological reports · 2026Article
- Respiratory Monitoring in Motion: An Overview of Wearable Methods and Algorithmic Approaches for Reliable Assessment.Biosensors · 2026Review
- A late fusion multi-task learning for respiratory waveform and rate estimation from photoplethysmography.PloS one · 2026Article
- 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
- Relationship between central autonomic effective connectivity and heart rate variability: A Resting-state fMRI dynamic causal modeling study.NeuroImage · 2024Article
- Parameter-Efficient Deep Learning Models for Vital Sign Estimation From PPG.Healthcare technology lettersArticle
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Authors and funding
7 authors.
Funding
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Abstract
Respiratory rate (RR) is a critical vital sign that can provide valuable insights into various medical conditions, including pneumonia. Unfortunately, manual RR counting is often unreliable and discontinuous. Current RR estimation algorithms either lack the necessary accuracy or demand extensive window sizes. In response to these challenges, this study introduces a novel method for continuously estimating RR from photoplethysmogram (PPG) with a reduced window size and lower processing requirements. To evaluate and compare classical and deep learning algorithms, this study leverages the BIDMC and CapnoBase datasets, employing the Respiratory Rate Estimation (RRest) toolbox. The optimal classical techniques combination on the BIDMC datasets achieves a mean absolute error (MAE) of 1.9 breaths/min. Additionally, the developed neural network model utilises convolutional and long short-term memory layers to estimate RR effectively. The best-performing model, with a 50% train-test split and a window size of 7 s, achieves an MAE of 2 breaths/min. Furthermore, compared to other deep learning algorithms with window sizes of 16, 32, and 64 s, this study's model demonstrates superior performance with a smaller window size. The study suggests that further research into more precise signal processing techniques may enhance RR estimation from PPG signals.
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