Bayesian denoising algorithm dealing with colored, non-stationary noise in continuous glucose monitoring timeseries.

ABSTRACT: Introduction: The retrospective analysis of continuous glucose monitoring (CGM) timeseries can be hampered by colored and non-stationary measurement noise. Here, we introduce a Bayesian denoising (BD) algorithm to address both autocorrelation of measurement noise and temporal variability of its variance. Methods: BD utilizes adaptive, a-priori models of signal and noise, whose unknown variances are derived on partially-overlapped CGM windows, via smoothing approach based on linear mean square estimation. The CGM signal and noise variability profiles are then reconstructed using a kernel smoother. BD is first assessed on two simulated datasets, D_S1 and D_S2. On D_S1, the effectiveness of accounting for colored noise is evaluated by compari

SUBMITTER: Camerlingo N 

PROVIDER: S-EPMC10703295 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature