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Artificial intelligence for non-invasive glycaemic-events detection via ECG in a paediatric population: study
Martina Andellini1, Salman Haleem1, Massimiliano Angelini2
1School of Engineering, University of Warwick, CV4 7AL Coventry, UK.
Insights
This study validates an AI algorithm using ECG to detect glucose events in children with Type 1 Diabetes (T1D). The non-invasive method aims to improve glycaemic control and reduce severe hypo/hyperglycaemic events.
Area of Science:
- Biomedical Engineering
- Artificial Intelligence in Healthcare
- Pediatric Endocrinology
Background:
- Children with Type 1 Diabetes (T1D) face risks from poor glycaemic control.
- Frequent blood glucose monitoring is crucial but can be invasive.
- Non-invasive techniques are being explored to monitor glucose levels.
Purpose of the Study:
- To validate an artificial intelligence (AI) algorithm for detecting glycaemic events.
- To utilize electrocardiogram (ECG) signals from a non-invasive device for glucose monitoring.
- To improve glycaemic management in pediatric T1D patients.
Main Methods:
- Enrolling pediatric T1D participants using Continuous Glucose Monitoring (CGM).
- Employing a non-invasive wearable device to record physiological data and respiratory rate.
- Developing and validating personalized and generalized deep learning (DL) AI classifiers using ECG variables.
Main Results:
- Data collection is expected to conclude by June 2023.
- Sufficient data will be gathered to develop and validate the AI algorithm.
- The study aims to demonstrate the AI's capability in detecting hypoglycaemic events from ECG data.
Conclusions:
- This validation study expands on pilot results from healthy adults.
- It will assess the AI algorithm's reliability in detecting glycaemic events in pediatric T1D patients.
- The research provides evidence for non-invasive glycaemic event detection in free-living conditions.
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.
Trial Registration:
ClinicalTrials.gov identifier: NCT03936634. Registered on 11 March 2022, retrospectively registered, https://www.clinicaltrials.gov/ct2/show/NCT05278143?titles=AI+for+Glycemic+Events+Detection+Via+ECG+in+a+Pediatric+Population&draw=2&rank=1.
Supplementary Information:
The online version contains supplementary material available at 10.1007/s12553-022-00719-x.
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