Work packages 2 - 5

Lead beneficiary : RU
The goal of this work package is to build secure cloud-based facilities to store and analyse data related to the project.   The work in this work package can be divided in to four parts.  Task 2.1: Generate a secure cloud-based data store  In this task, we will construct a computer infrastructure for all the research data in the project. The equipment provides a secure, access-controlled storage, including a high-performance GPU-based cluster resource for researchers to process massive amounts of data and train deep neural networks.

Lead beneficiary : UQ
The goal of this work package is to develop methods that go through sleep recordings and score them automatically using machine learning methods. This means for example to identify the sleep stages throughout the night and finding events such as breathing difficulties and apnoeas. Currently, it takes a trained sleep technologist two hours to do this manually, but with automated methods this can be done, we can achieve this in a matter of seconds, and the sleep technologists will only have to give their feedback on the grey areas where the methods is not able to give a clear answer.

Lead beneficiary : RU
The focus of WP4 is on the standardisation of current sleep study scoring and the development of state-of-the art machine learning solutions and a standardised European Sleep Scoring Manual for the scoring of the sleep studies. Scoring of biosignals during sleep, whether collected at home or in a laboratory, is ideally performed by trained sleep technologists following guidelines published by the American Academy of Sleep Medicine (AASM).

Lead beneficiary : UEF
The current diagnostic practice for obstructive sleep apnea is based on metrics developed decades ago. These were limited by the analog recordings printed on paper and simplistic analyses. However, these metrics remain diagnostic cornerstones despite the advancements in digital recordings and computational methods. To overcome the limitations in the current clinical practice, we aim to develop artificial intelligence-based methods for the estimation of the severity of obstructive sleep apnea and related severe health consequences.