Name
Title | ||
|---|---|---|
Data-Driven Supervised Compression Artifacts Detection on Continuous Glucose Sensors. |
Abstract | ||
|---|---|---|
Continuous Glucose Monitoring (CGM) sensors micro-invasively provide frequent glucose readings, improving the management of Type 1 diabetic patients' life and making available reach data-sets for retrospective analysis. Unlikely, CGM sensors are subject to failures, such as compression artifacts, that might impact on both real-time and respective CGM use. In this work is focused on retrospective detection of compression artifacts. An in-silico dataset is generated using the T1D UVa/Padova simulator and compression artifacts are subsequently added in known position, thus creating a dataset with perfectly accurate faulty/not-faulty labels. The problem of compression artifact detection is then faced with supervised data-driven techniques, in particular using Random Forest algorithm. The detection performance guaranteed by the method on in-silico data is satisfactory, opening the way for further analysis on real-data. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/EMBC48229.2022.9870884 | Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) |
DocType | Volume | ISSN |
Conference | 2022 | 2694-0604 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Elena Idi | 1 | 0 | 0.34 |
| Eleonora Manzoni | 2 | 0 | 0.34 |
| Giovanni Sparacino | 3 | 0 | 1.35 |
| Simone Del Favero | 4 | 0 | 0.68 |