The Library
Artificial intelligence for non-invasive glycaemic-events detection via ECG in a paediatric population : study protocol
Tools
Tools
Tools
Andellini, Martina, Haleem, Salman, Angelini, Massimiliano, Ritrovato, Matteo, Schiaffini, Riccardo, Iadanza, Ernesto and Pecchia, Leandro (2023) Artificial intelligence for non-invasive glycaemic-events detection via ECG in a paediatric population : study protocol. Health and Technology, 13 . pp. 145-154. doi:10.1007/s12553-022-00719-x ISSN 2190-7196.
|
PDF
s12553-022-00719-x.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (1015Kb) | Preview |
Official URL: https://doi.org/10.1007/s12553-022-00719-x
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 wear an additional non-invasive wearable device for recording physiological data and respiratory rate. Glycaemic measurements driven through ECG variables are the main outcomes. Data collected will be used to design, develop and validate the personalised and generalized classifiers based on a deep learning (DL) AI algorithm, able to automatically detect hypoglycaemic events by using few ECG heartbeats recorded with wearable devices.
Results
Data collection is expected to be completed approximately by June 2023. It is expected that sufficient data will be collected to develop and validate the AI algorithm.
Conclusion
This is a validation study that will perform additional tests on a larger diabetes sample population to validate the previous pilot results that were based on four healthy adults, providing evidence on the reliability of the AI algorithm in detecting glycaemic events in paediatric diabetic patients in free-living conditions.
| Item Type: | Journal Article | ||||||
|---|---|---|---|---|---|---|---|
| Subjects: | R Medicine > RA Public aspects of medicine R Medicine > RC Internal medicine T Technology > TK Electrical engineering. Electronics Nuclear engineering |
||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||
| SWORD Depositor: | Library Publications Router | ||||||
| Library of Congress Subject Headings (LCSH): | Blood glucose monitoring, Electrocardiography -- Data processing, Signal processing -- Digital techniques, Diabetes | ||||||
| Journal or Publication Title: | Health and Technology | ||||||
| Publisher: | Springer | ||||||
| ISSN: | 2190-7196 | ||||||
| Official Date: | 23 January 2023 | ||||||
| Dates: |
|
||||||
| Volume: | 13 | ||||||
| Page Range: | pp. 145-154 | ||||||
| DOI: | 10.1007/s12553-022-00719-x | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||
| Date of first compliant deposit: | 24 February 2023 | ||||||
| Date of first compliant Open Access: | 24 February 2023 | ||||||
| RIOXX Funder/Project Grant: |
|
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
Downloads
Downloads per month over past year
