Artificial Intelligence for Glycemic Events Detection Via Electrocardiogram in a Pediatric Population
Artificial Intelligence for Glycemic Events Detection Via Electrocardiogram in a Pediatric Population
Paediatric Type 1 Diabetes (T1D) patients are at greater risk for developing severe hypo and hyperglycaemic events due to poor glycaemic control and incorrect Insulin administration. To reduce the risk of adverse events, patients need to achieve the best possible glycaemic control 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 a deep learning algorithm to detect glycaemic events using electrocardiogram (ECG) signals collected through non-invasive device.
This observational single-arm study will enrol participants with T1D aged less than 18 years old who already use CGM device. Participants will wear an additional non-invasive wearable device, for recording physiological data (e.g. ECG, breathing waveform, 3-axis acceleration) for three days. ECG variables (e.g. heart rate variability features), respiratory rate, physical activity, posture and glycaemic measurements driven through ECG variables and other physiological signals (e.g. the frequency of hypo or hyperglycaemic events, the time spent in hypo- or hyperglycaemia and the time in range) are the main outcomes. A quality-of-life questionnaire will be administered to collect secondary outcomes. Data collected will be used to design, develop and validate the personalised and generalized classifiers based on a deep-learning artificial intelligence (AI) algorithm developed during the pilot study, able to automatically detect hypoglycaemic events by using few ECG heartbeats recorded with wearable devices.
This study is a validation study that will carry out additional tests on a larger diabetes sample population, to validate the previous promising pilot results that were based on four healthy adult subjects. Therefore, this study will provide evidence on the reliability of the deep-learning artificial intelligence algorithms investigators developed, in detecting glycaemic events in paediatric diabetic patients in free-living conditions. Additionally, this study aims to develop the generalized AI model for the automated glycaemic events detection on real-time ECG.
As per inclusion criteria, the study participants continue to use their CGM device they are already using. During their routine diabetes hospital visit, the participants are asked to wear an additional wearable device, Medtronic Zephyr BioPatch, for recording the physiological data for a period of up to three days. After receiving the training session and relevant information about the study, the participants are allowed to return home with the wearable device attached. During the hospital visit, the quality of life questionnaire for paediatric patients (PEdsQL) is submitted to recruited patients. They are asked to answer questions on how T1D affects their daily activities.
During the monitoring days, patients can continue their daily activities undisturbed, without any changes in either physical activities or diet. In this way, data gathered from free-living conditions are obtained. They should wear the sensor during the day and the night and remove it while showering. The device should be approximately charged every 12-hours. For this reason, patients were provided with two devices. While wearing the second device the one used during the day should be recharged and vice versa. Patients receive regular contact from the research team not only to check on their safety and wellbeing, but also to ensure the data collection is successful. At the end of the third day, patients should return the devices to the hospital.
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martina.andellini@opbg.net+393357625261