Respiratory Rate Estimation Using the Photoplethysmogram: Towards the Implementation in Wearables

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Respiratory Rate Estimation Using the Photoplethysmogram: Towards the Implementation in Wearables

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Respiratory Rate Estimation Using the Photoplethysmogram: Towards the Implementation in Wearables

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20601 - Medical engineering

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2021 Computing in Cardiology (CinC)

ISBN

978-166547916-5

ISSN

2325-8861

e-ISSN

2325-887X

Počet stran výsledku

4

Strana od-do

15

Název nakladatele

IEEE

Místo vydání

New York

Místo konání akce

Brno

Datum konání akce

12. 9. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—