Evidence map›Paper›PMID 30384462›Full record

ArticleSensors (Basel, Switzerland)2018

Validation of Instantaneous Respiratory Rate Using Reflectance PPG from Different Body Positions.

Delaram Jarchi, Dario Salvi, Lionel Tarassenko, David A Clifton

Abstract readValidation Study
In one paragraph

Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Delaram JarchiDepartment of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford OX3 7DQ, UK. delaram.jarchi@eng.ox.ac.uk.ORCID 0000-0001-6699-8721
Dario SalviDepartment of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford OX3 7DQ, UK. dario.salvi@eng.ox.ac.uk.ORCID 0000-0002-9203-1124
Lionel TarassenkoDepartment of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford OX3 7DQ, UK. lionel.tarassenko@eng.ox.ac.uk.
David A CliftonDepartment of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford OX3 7DQ, UK. davidc@robots.ox.ac.uk.

Funding

Engineering and Physical Sciences Research Council EP/N024966/1
6 · The paper itself

Abstract

Respiratory rate (RR) is a key parameter used in healthcare for monitoring and predicting patient deterioration. However, continuous and automatic estimation of this parameter from wearable sensors is still a challenging task. Various methods have been proposed to estimate RR from wearable sensors using windowed segments of the data; e.g., often using a minimum of 32 s. Little research has been reported in the literature concerning the instantaneous detection of respiratory rate from such sources. In this paper, we develop and evaluate a method to estimate instantaneous respiratory rate (IRR) from body-worn reflectance photoplethysmography (PPG) sensors. The proposed method relies on a nonlinear time-frequency representation, termed the wavelet synchrosqueezed transform (WSST). We apply the latter to derived modulations of the PPG that arise from the act of breathing.We validate the proposed algorithm using (i) a custom device with a PPG probe placed on various body positions and (ii) a commercial wrist-worn device (WaveletHealth Inc., Mountain View, CA, USA). Comparator reference data were obtained via a thermocouple placed under the nostrils, providing ground-truth information concerning respiration cycles. Tracking instantaneous frequencies was performed in the joint time-frequency spectrum of the (4 Hz re-sampled) respiratory-induced modulation using the WSST, from data obtained from 10 healthy subjects. The estimated instantaneous respiratory rates have shown to be highly correlated with breath-by-breath variations derived from the reference signals. The proposed method produced more accurate results compared to averaged RR obtained using 32 s windows investigated with overlap between successive windows of (i) zero and (ii) 28 s. For a set of five healthy subjects, the averaged similarity between reference RR and instantaneous RR, given by the longest common subsequence (LCSS) algorithm, was calculated as 0.69; this compares with averaged similarity of 0.49 using 32 s windows with 28 s overlap between successive windows. The results provide insight into estimation of IRR and show that upper body positions produced PPG signals from which a better respiration signal was extracted than for other body locations.

Indexed as

AdultAlgorithmsFemaleHumansMalePhotoplethysmographyPostureReproducibility of ResultsRespiratory RateSignal Processing, Computer-AssistedWavelet AnalysisWearable Electronic DevicesmodulationPPGrespiratory ratewavelet synchrosqueezed transform

Identifiers

PMID30384462
PMCPMC6264115

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.