CGM-Freq: A Python Library for Frequency Domain Analysis of Continuous Glucose Monitoring Data

Elizabeth Healey, Isaac S. Kohane

Keywords: Signal processing, Physiological data, Diabetes, Biomarkers, Wearable technology

TL;DR: This paper presents an open-source python tool to generate digital biomarkers from the frequency domain of continuous glucose monitoring data and the results of testing on a real dataset.

Abstract:

In recent years, the number of patients using continuous
glucose monitoring (CGM) has increased. In addition
to helping patients manage their disease, CGM produces time
series data that can be used for integration in control algorithms,
predictive models, and for retrospective analyses. Through feature
extraction, many digital biomarkers can be derived from
CGM. In this work, we provide a tool to extract features derived
from the frequency domain. We first introduce a novel open-source
Python library, CGM-Freq, for the analysis of CGM
data in the frequency domain. We then test the library on real
data. This work provides an open-source tool to further investigate the
frequency domain of CGM signals.

Track: 10. Digital health

Registration Id: P4NT22NVBW4

Submission Number: 331