Respiratory Rate Estimation Using the Photoplethysmogram: Towards the Implementation in Wearables
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081731%3A_____%2F21%3A00555027" target="_blank" >RIV/68081731:_____/21:00555027 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/9662674" target="_blank" >https://ieeexplore.ieee.org/document/9662674</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.23919/CinC53138.2021.9662674" target="_blank" >10.23919/CinC53138.2021.9662674</a>
Alternative languages
Result language
angličtina
Original language name
Respiratory Rate Estimation Using the Photoplethysmogram: Towards the Implementation in Wearables
Original language description
Respiratory rate (RR) is one of the most important physiological parameters. In recent years, the RR estimation from PPGs widely used in smart devices has been promoted. The effect of respiration on PPGs manifests in three ways: BW (intensity variation), AM (amplitude variation), FM (frequency variation). In addition to sophisticated RR estimation methods, reliable results can be achieved with simple and efficient methods implementable in wearables. The BW signal (respiratory signal estimation, RS) can be obtained by linear filtering of the PPG. The RR estimation is based on BW extremes (sBW), BW autocorrelation extremes (aBW) and their spectra (SBW, ABW). Estimation of the AM RS requires PPG extremes detection and interpolation. The RR estimation is based on extremes of the AM signal (sAM), its autocorrelation (aAM) and their spectra (SAM, AAM). The fusion of RR estimates leads to more robust results. To test the algorithms, the annotated BIDMC and CapnoBase Datasets were used. RR estimates were made for 60 s sections. The simplest and the most accurate method for both datasets is the RR estimation based on sBW (RsBW). The median absolute error was 0.40 (0.16-1.09 interquartile range 25-75th) bpm for the 60s window, mean absolute error was 1.42 bpm.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
20601 - Medical engineering
Result continuities
Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2021 Computing in Cardiology (CinC)
ISBN
978-166547916-5
ISSN
2325-8861
e-ISSN
2325-887X
Number of pages
4
Pages from-to
15
Publisher name
IEEE
Place of publication
New York
Event location
Brno
Event date
Sep 12, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—