Artificial intelligence for non-invasive glycaemic-events detection via ECG in a paediatric population: study protocol.

ABSTRACT:

Purpose

Paediatric Type 1 Diabetes (T1D) patients are at greater risk for developing severe hypo and hyperglycaemic events due to poor glycaemic control. To reduce the risk of adverse events, patients need to achieve the best possible glycaemic management through frequent blood glucose monitoring with finger prick or Continuous Glucose Monitoring (CGM) systems. However, several non-invasive techniques have been proposed aiming at exploiting changes in physiological parameters based on glucose levels. The overall objective of this study is to validate an artificial intelligence (AI) based algorithm to detect glycaemic events using ECG signals collected through non-invasive device.

Methods

This study will enrol T1D paediatric participants who already use CGM. Participants will

SUBMITTER: Andellini M 

PROVIDER: S-EPMC9899724 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature