DEEP LEARNING AND PHYSIOLOGY BASED APPROACH TO DERIVE AND LINK OBSTRUCTIVE SLEEP APNOEA PHENOTYPES AND SYMPTOMATOLOGY

Funding Activity

Website
http://purl.org/au-research/grants/nhmrc/2001729

Funding Status
Status not available

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Funded Activity Summary

Obstructive sleep apnoea (OSA) is a highly prevalent nocturnal breathing disorder strongly related to daytime sleepiness, accident risk and reduced quality of life. However, the current severity index, the apnoea-hypopnoea index, poorly predicts daytime sleepiness and vigilance. In this project we elegantly combine physiological insight and artificial intelligence to develop and evaluate novel clinically applicable computational tools for detailed quantification of OSA severity and its symptoms.

Funded Activity Details

Start Date: 01-01-2020

End Date: 01-01-2023

Funding Scheme: Ideas Grants

Funding Amount: $402,978.00

Funder: National Health and Medical Research Council

Research Topics

ANZSRC Field of Research (FoR)

ANZSRC Socio-Economic Objective (SEO)

There are no SEO codes available for this funding activity

Other Keywords

artificial neural networks | biomedical engineering | daytime sleepiness | obstructive sleep apnoea

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