Breathing Rate Estimation From the Electrocardiogram and Photoplethysmogram: A Review

@article{Charlton2018BreathingRE,
  title={Breathing Rate Estimation From the Electrocardiogram and Photoplethysmogram: A Review},
  author={Peter H. Charlton and Drew A. Birrenkott and Timothy Bonnici and Marco A. F. Pimentel and Alistair E. W. Johnson and Jordi Alastruey and Lionel Tarassenko and Peter J. Watkinson and Richard Beale and David A. Clifton},
  journal={IEEE reviews in biomedical engineering},
  year={2018},
  volume={11},
  pages={2 - 20}
}
Breathing rate (BR) is a key physiological parameter used in a range of clinical settings. Despite its diagnostic and prognostic value, it is still widely measured by counting breaths manually. A plethora of algorithms have been proposed to estimate BR from the electrocardiogram (ECG) and pulse oximetry (photoplethysmogram, PPG) signals. These BR algorithms provide opportunity for automated, electronic, and unobtrusive measurement of BR in both healthcare and fitness monitoring. This paper… 
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Results reveal that the location of the ECG electrodes between auscultation sites does not impact the estimation of RR, and baseline wander and amplitude modulation algorithms outperformed the frequency modulation and band-pass filter algorithms, paving the way for ECG-based RR estimation in miniaturised integrated cardiac screening device.
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Results appear to indicate that the contact iPPG method implemented directly on the smartphone is a good option, accessible to the common population to estimate the instantaneous HR and RR values outside specialized clinical environments, e.g., in the point-of-contact office.
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This paper describes a novel approach to RR estimation using machine learning (ML) models with the PPG signal features and exhibits a root mean squared error (RMSE), mean absolute error (MAE), and two-standard deviation (2SD) of 2.57, 1.91, and 5.13 breaths per minute, respectively.
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