Project CESAR

The Cesar project (2017 – 2021) started with the hypothesis that low-cost solutions based on smartphones, wearables, and machine learning (ML) can contribute to decrease the number of undiagnosed sleep apnoea (SA) patients (which is currently around 80%). The project team at Ifi and the University Hospital of Oslo could give through a clinical study strong evidence that the hypothesis is correct. To achieve this many challenges had to be addressed in the entire pipeline from data acquisition, quantifying the quality of data from wearables, to finally create ML models that can properly classify sleep monitoring data with respect to SA events. In the presentation, we aim to give the audience an introduction into SA and its diagnosis, the various (technical and interdisciplinary) challenges we addressed, and the main results.