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Advancing PPG Signal Quality and Know-How Through Knowledge Translation—From Experts to Student and Researcher

Samuel Huthart 1
Mohamed Elgendi 2
Dingchang Zheng 3
Gerard Stansby 1, 4
John Allen 1, 3, 5
Publication typeJournal Article
Publication date2020-12-21
scimago Q1
wos Q1
SJR1.070
CiteScore6.3
Impact factor3.8
ISSN2673253X
Abstract

Objective: Despite the vast number of photoplethysmography (PPG) research publications and growing demands for such sensing in Digital and Wearable Health platforms, there appears little published on signal quality expectations for morphological pulse analysis. Aim: to determine a consensus regarding the minimum number of undistorted i.e., diagnostic quality pulses required, as well as a threshold proportion of noisy beats for recording rejection.

Approach: Questionnaire distributed to international fellow researchers in skin contact PPG measurements on signal quality expectations and associated factors concerning recording length, expected artifact-free pulses (“diagnostic quality”) in a trace, proportion of trace having artifact to justify excluding/repeating measurements, minimum beats required, and number of respiratory cycles.

Main Results: 18 (of 26) PPG researchers responded. Modal range estimates considered a 2-min recording time as target for morphological analysis. Respondents expected a recording to have 86–95% of diagnostic quality pulses, at least 11–20 sequential pulses of diagnostic quality and advocated a 26–50% noise threshold for recording rejection. There were broader responses found for the required number of undistorted beats (although a modal range of 51–60 beats for both finger and toe sites was indicated).

Significance: For morphological PPG pulse wave analysis recording acceptability was indicated if <50% of beats have artifact and preferably that a minimum of 50 non-distorted PPG pulses are present (with at least 11–20 sequential) to be of diagnostic quality. Estimates from this knowledge transfer exercise should help inform students and researchers as a guide in standards development for PPG study design.

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GOST Copy
Huthart S. et al. Advancing PPG Signal Quality and Know-How Through Knowledge Translation—From Experts to Student and Researcher // Frontiers in Digital Health. 2020. Vol. 2.
GOST all authors (up to 50) Copy
Huthart S., Elgendi M., Zheng D., Stansby G., Allen J. Advancing PPG Signal Quality and Know-How Through Knowledge Translation—From Experts to Student and Researcher // Frontiers in Digital Health. 2020. Vol. 2.
RIS |
Cite this
RIS Copy
TY - JOUR
DO - 10.3389/fdgth.2020.619692
UR - https://doi.org/10.3389/fdgth.2020.619692
TI - Advancing PPG Signal Quality and Know-How Through Knowledge Translation—From Experts to Student and Researcher
T2 - Frontiers in Digital Health
AU - Huthart, Samuel
AU - Elgendi, Mohamed
AU - Zheng, Dingchang
AU - Stansby, Gerard
AU - Allen, John
PY - 2020
DA - 2020/12/21
PB - Frontiers Media S.A.
VL - 2
PMID - 34713077
SN - 2673-253X
ER -
BibTex
Cite this
BibTex (up to 50 authors) Copy
@article{2020_Huthart,
author = {Samuel Huthart and Mohamed Elgendi and Dingchang Zheng and Gerard Stansby and John Allen},
title = {Advancing PPG Signal Quality and Know-How Through Knowledge Translation—From Experts to Student and Researcher},
journal = {Frontiers in Digital Health},
year = {2020},
volume = {2},
publisher = {Frontiers Media S.A.},
month = {dec},
url = {https://doi.org/10.3389/fdgth.2020.619692},
doi = {10.3389/fdgth.2020.619692}
}