Uncertainty Quantification for Machine Learning Models Applied to Photoplethysmography Signals

Provider

Ministry of Education, Youth and Sports

Programme

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Call for proposals

Call 2022

Main participants

Český metrologický institut

Contest type

RP - Co-financing of EC programme

Contract ID

MSMT-18154/2023-3/6

Project name in Czech

Uncertainty quantification for machine learning models applied to photoplethysmography signals

Annotation in Czech

The overall objective is to provide trustworthy machine learning models for analysing photoplethysmography signals in a medical context, by developing methods for the quantification of uncertainty in supervised machine learning and deep learning models applied to photoplethysmography signals and generating reference datasets to benchmark those models, supported by software being developed that will be publicly available for independent review of the models.

R&D category

IF - RDI infrastructure

OECD FORD - main branch

10601 - Cell biology

OECD FORD - secondary branch

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OECD FORD - another secondary branch

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CEP - equivalent branches <br>(according to the <a href="http://www.vyzkum.cz/storage/att/E6EF7938F0E854BAE520AC119FB22E8D/Prevodnik_oboru_Frascati.pdf">converter</a>)

EA - Morphology and cytology

Realization period - beginning

Jul 1, 2023

Realization period - end

Jun 30, 2026

Project status

B - Running multi-year project

Latest support payment

Feb 14, 2025

Confidentiality

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

Data delivery code

CEP25-MSM-9B-R

Data delivery date

Mar 5, 2025

Total approved costs

4,344 thou. CZK

Public financial support

2,172 thou. CZK

Other public sources

0 thou. CZK

Non public and foreign sources

0 thou. CZK