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Towards a Robust Estimation of Respiratory Rate from Pulse Oximeters

Research output: Contribution to journalArticle

Marco Pimentel, Alistair Johnson, Peter Harcourt Charlton, Drew Birrenkott, Peter Watkinson, Lionel Tarassenko, David Clifton

Original languageEnglish
Pages (from-to)1914-1923
JournalIEEE Transactions on Biomedical Engineering
Issue number8
StatePublished - Aug 2016


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Research outputs


Objective: Current methods for estimating respiratory rate (RR) from the photoplethysmogram (PPG) typically fail to distinguish between periods of high- and low-quality input data, and fail to perform well on independent “validation” datasets. The lack of robustness of existing methods directly results in a lack of penetration of such systems into clinical practice. The present work proposes an alternative method to improve the robustness of the estimation of RR from the PPG.
Methods: The proposed algorithm is based on the use of multiple autoregressive models of different orders for determining the dominant respiratory frequency in the three respiratory-induced variations (frequency, amplitude and intensity) derived from the PPG. The algorithm was tested on two different datasets comprising 95 8-minute PPG recordings (in total) acquired from both children and adults in different clinical settings, and its performance using two window sizes (32 and 64 seconds) was compared with that of existing methods in the literature.
Results: The proposed method achieved comparable accuracy to existing methods in the literature, with mean absolute errors (median, 25th-75th percentiles for a window size of 32 seconds) of 1.5 (0.3- 3.3) and 4.0 (1.8-5.5) breaths per minute (for each dataset respectively), whilst providing RR estimates for a greater proportion of windows (over 90% of the input data are kept).
Conclusion: Increased robustness of RR estimation by the proposed method was demonstrated.
Significance: This work demonstrates that the use of large publicly-available datasets is essential for improving the robustness of wearable-monitoring algorithms for use in clinical practice.

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