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Breathing Rate Estimation from the Electrocardiogram and Photoplethysmogram: A Review

Research output: Contribution to journalReview articlepeer-review

Peter Harcourt Charlton, Drew Birrenkott, Timothy Alexander Bonnici, Marco Pimentel, Alistair Johnson, Jordi Alastruey-Arimon, Lionel Tarassenko, Peter Watkinson, Richard Beale, David Clifton

Original languageEnglish
Number of pages17
JournalIEEE Reviews in Biomedical Engineering
Volume99
Issue numberPP
Early online date6 Sep 2017
DOIs
Accepted/In press15 Jul 2017
E-pub ahead of print6 Sep 2017
Published24 Oct 2017

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Abstract

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 presents a review of the literature on BR estimation from the ECG and PPG. Firstly, the structure of BR algorithms and the mathematical techniques used at each stage are described. Secondly, the experimental methodologies which have been used to assess the performance of BR algorithms are reviewed, and a methodological framework for the assessment of BR algorithms is presented. Thirdly, we outline the most pressing directions for future research, including the steps required to use BR algorithms in wearable sensors, remote video monitoring, and clinical practice.

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