ArticleSensors (Basel, Switzerland)2025
Comparison of Techniques for Respiratory Rate Extraction from Electrocardiogram and Photoplethysmogram.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Beyond Single-Lead ECG-Derived Respiration Analysis: Use of Vectorcardiograms from the EASI-System for Breathing Frequency Estimation-A Feasibility Study.Sensors (Basel, Switzerland) · 2026Article
- Respiratory signal extraction from the electrocardiogram for risk stratification in cardiac patients.European heart journal. Digital health · 2026Review
- Respiratory Monitoring in Motion: An Overview of Wearable Methods and Algorithmic Approaches for Reliable Assessment.Biosensors · 2026Review
- Evaluation of Mindful Breathing Method Using Tablet Devices Through Chaos Analysis and Frequency Analysis: A Pilot Randomized Controlled Trial.Biomedical engineering and computational biology · 2026Article
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Authors and funding
9 authors.
Funding
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Abstract
backgroundRespiratory rate (RR) is a key vital sign and one of the most sensitive indicators of physiological conditions, playing a crucial role in the early identification of clinical deterioration. The monitoring of RR using electrocardiography (ECG) and photoplethysmography (PPG) aims to overcome limitations of traditional methods in clinical settings.
methodsThe proposed approach extracts RR from ECG and PPG signals using different morphological and temporal features from publicly available datasets (iAMwell and Capnobase). The algorithm was used to develop and test with a selection of relevant ECG (e.g., R-peak, QRS area, and QRS slope) and PPG (amplitude and frequency modulation) characteristics.
resultsThe results show promising performance, with the ECG-derived signal using the R-peak-based method yielding the lowest error, with a mean absolute error of 0.99 breaths/min in the iAMwell dataset and 3.07 breaths/min in the Capnobase dataset. In comparison, the RR PPG-derived signal showed higher errors of 5.10 breaths/min in the iAMwell dataset and 10.66 breaths/min in the Capnobase dataset, for the FM and AM method, respectively. Bland-Altman analysis revealed a small negative bias, approximately -0.97 breaths/min for the iAMwell dataset (with limits of agreement from -2.62 to 0.95) and -1.16 breaths/min for the Capnobase dataset (limits of agreement from -3.37 to 1.10) in the intra-subject analysis. In the inter-subject analysis, the bias was -0.84 breaths/min (limits of agreement from -1.76 to 0.20) for iAMwell and -1.22 breaths/min (limits of agreement from -7.91 to 5.35) for Capnobase, indicating a slight underestimation. Conversely, the PPG-derived signal tended to overestimate RR, resulting in higher variability and reduced accuracy. These findings highlight the higher reliability of ECG-derived features for RR estimation in the analyzed datasets.
conclusionThis study suggests that the proposed approach could guide the design of cost-effective, non-invasive methods for continuous respiration monitoring, offering a reliable tool for detecting conditions like stress, anxiety, and sleep disorders.
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