Key result
The Energy Variance Maximization (EVM) algorithm improved respiratory rate assessment compared to state-of-the-art methods, with a Mean Absolute Error 0.87 rpm better than CHROM.
Why the study?
Remote photoplethysmography (rPPG) allows non-contact respiratory rate monitoring, but its signal quality is lower than contact-based methods.
Effect estimate: MAE improvement 0.87 rpm
The novel EVM algorithm improves the accuracy of non-contact respiratory rate monitoring using remote photoplethysmography compared to existing methods.
May enable contactless monitoring; leaves open prospective clinical validation before adoption.
Monitoring of breathing rate considerably improves healthcare quality by providing information about the patient’s state. Yet, the current methods used to assess the respiratory rate have some drawbacks that could be improved. Recent developments of the non-contact based measurement of the respiratory rate brought the remote Photoplethysmography (rPPG) technique. This technique allows physiological parameters monitoring with a camera and ambient light. It measures the light absorption variation by the blood to extract several physiological parameters. The main issue with the rPPG method is that its signal quality is lower compared with the contact-based methods. Several ways to improve this quality may be explored to obtain a robust rPPG pipeline. One of these is the combination of the multidimensional temporal traces generated by a camera into a single temporal trace. The literature methods aimed to extract a signal with the maximal information using smart estimation of the combination coefficients. These coefficients are computed using statistical or physiological/optical properties of the traces. In this paper, we developed an algorithm that estimates a better respiratory signal than with regular methods. The algorithm named Energy Variance Maximization (EVM) estimates the linear combination that maximizes the Signal Noise Ratio (SNR) of the output trace. We compare our contribution with three state-of-the-art method: CHROM, PBV and PVM. The results obtained with this method are better than the state of the art methods, with the Mean Absolute Error (MAE) being 0.87 rpm better than CHROM, the best of compared literature methods.
No takes yet. Share an insight, caveat, or question.
Luguern et al. (2020) studied Respiratory rate assessment. Energy Variance Maximization (EVM) algorithm vs. CHROM, PBV, and PVM methods was evaluated on Mean Absolute Error (MAE) of respiratory rate (MAE improvement 0.87 rpm). The Energy Variance Maximization (EVM) algorithm improved respiratory rate assessment compared to state-of-the-art methods, with a Mean Absolute Error 0.87 rpm better than CHROM.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: