Unannounced Meal Detection for Artificial Pancreas Systems Using Extended Isolation Forest

Fei Zheng (1, 2) , Stéphane Bonnet (2) , Emma Villeneuve (2) , Maeva Doron (2) , Aurore Lepecq (2) , Florence Forbes (1)

1 STATIFY - Modèles statistiques bayésiens et des valeurs extrêmes pour données structurées et de grande dimension
2 DTBS - Département Microtechnologies pour la Biologie et la Santé

This study aims at developing an unannounced meal detection method for artificial pancreas, based on a recent extension of Isolation Forest. The proposed method makes use of features accounting for individual Continuous Glucose Monitoring (CGM) profiles and benefits from a two-threshold decision rule detection. The advantage of using Extended Isolation Forest (EIF) instead of the standard one is supported by experiments on data from virtual diabetic patients, showing good detection accuracy with acceptable detection delays.

Domaines: Statistiques [math.ST]

Fei Zheng, Stéphane Bonnet, Emma Villeneuve, Maeva Doron, Aurore Lepecq, et al.. Unannounced Meal Detection for Artificial Pancreas Systems Using Extended Isolation Forest. 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jul 2020, Montreal, Canada. ⟨10.1109/EMBC44109.2020.9176856⟩. ⟨hal-03874078⟩