Finding Optimal Alphabet for Encoding Daily Continuous Glucose Monitoring Time Series Into Compressed Text.

ABSTRACT:

Background

The emergence of continuous glucose monitoring (CGM) devices has not only revolutionized diabetes management but has also opened new avenues for research. This article presents a novel approach to encoding a CGM daily profile into a CGM string and CGM text that preserves clinical metrics information but compresses the data.

Methods

Eight alphabets were defined to represent glucose ranges. The Akaike information criterion (AIC) was derived from error, and the compression ratio was estimated for each alphabet to determine the optimal alphabet for encoding the CGM daily profile. The analysis was done with data from six distinct studies, with different treatment modalities, applied to individuals with type 1 diabetes (T1D) or type 2 diabetes (T2D), and without diabete

SUBMITTER: Igbe T 

PROVIDER: S-EPMC11924066 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature