title: Non-linear dynamic modeling of glucose in type 1 diabetes with kernel adaptive filters.
abstract: We propose a non-linear recursive solution to the problem of short-term prediction of glucose in type 1 diabetes. The Fixed Budget Quantized Kernel Least Mean Square (QKLMS-FB) algorithm is employed to construct a univariate model of subcutaneous glucose concentration, which: (i) handles nonlinearities by transforming the input space into a high-dimensional Reproducing Kernel Hilbert Space and, (ii) finds a sparse solution by retaining a representative subset of the training input vectors. The dataset comes from the continuous multi-day recordings of 15 type 1 patients in free-living conditions. QKLMS-FB produces an average root mean squared error of 18.66±3.19 mg/dl for a prediction horizon of 30 min with 82.04% of hypoglycemic readings and 93.30% of hyperglycemic ones being classified as clinically accurate or with benign errors. The effect of the prediction horizon is more evident in the hypoglycemic range.
authors.forename: Eleni I
authors.initials: EI
authors.lastname: Georga
authors.forename: Jose C
authors.initials: JC
authors.lastname: Principe
authors.forename: Demosthenes
authors.initials: D
authors.lastname: Polyzos
authors.forename: Dimitrios I
authors.initials: DI
authors.lastname: Fotiadis
mesh_terms: D000328:Adult; D000465:Algorithms; D001786:Blood Glucose / Q000032:analysis*; D003922:Diabetes Mellitus, Type 1 / Q000097:blood*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D008991:Monitoring, Physiologic; D017711:Nonlinear Dynamics
chemical_list: D001786:Blood Glucose