Development of an artificial intelligence system to identify hypoglycaemia via ECG in adults with type 1 diabetes: protocol for data collection under controlled and free-living conditions
Introduction Hypoglycaemia is a harmful potential complication in people with type 1 diabetes mellitus (T1DM) and can be exacerbated in patients receiving treatment, such as insulin therapies, by the very interventions aiming to achieve optimal blood glucose levels. Symptoms can vary greatly, includ...
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| Format: | Article |
| Language: | English |
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BMJ Publishing Group
2023-04-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/13/4/e067899.full |
| _version_ | 1797319292645015552 |
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| author | Martin O Weickert Thomas M Barber Owain Cisuelo Katy Stokes Iyabosola B Oronti Muhammad Salman Haleem Leandro Pecchia John Hattersley |
| author_facet | Martin O Weickert Thomas M Barber Owain Cisuelo Katy Stokes Iyabosola B Oronti Muhammad Salman Haleem Leandro Pecchia John Hattersley |
| author_sort | Martin O Weickert |
| collection | DOAJ |
| description | Introduction Hypoglycaemia is a harmful potential complication in people with type 1 diabetes mellitus (T1DM) and can be exacerbated in patients receiving treatment, such as insulin therapies, by the very interventions aiming to achieve optimal blood glucose levels. Symptoms can vary greatly, including, but not limited to, trembling, palpitations, sweating, dry mouth, confusion, seizures, coma, brain damage or even death if untreated. A pilot study with healthy (euglycaemic) participants previously demonstrated that hypoglycaemia can be detected non-invasively with artificial intelligence (AI) using physiological signals obtained from wearable sensors. This protocol provides a methodological description of an observational study for obtaining physiological data from people with T1DM. The aim of this work is to further improve the previously developed AI model and validate its performance for glycaemic event detection in people with T1DM. Such a model could be suitable for integrating into a continuous, non-invasive, glucose monitoring system, contributing towards improving surveillance and management of blood glucose for people with diabetes.Methods and analysis This observational study aims to recruit 30 patients with T1DM from a diabetes outpatient clinic at the University Hospital Coventry and Warwickshire for a two-phase study. The first phase involves attending an inpatient protocol for up to 36 hours in a calorimetry room under controlled conditions, followed by a phase of free-living, for up to 3 days, in which participants will go about their normal daily activities unrestricted. Throughout the study, the participants will wear wearable sensors to measure and record physiological signals (eg, ECG and continuous glucose monitor). Data collected will be used to develop and validate an AI model using state-of-the-art deep learning methods.Ethics and dissemination This study has received ethical approval from National Research Ethics Service (ref: 17/NW/0277). The findings will be disseminated via peer-reviewed journals and presented at scientific conferences.Trial registration number NCT05461144. |
| first_indexed | 2024-03-08T04:04:48Z |
| format | Article |
| id | doaj.art-922e69e583c549558ea38b1fad7b7de8 |
| institution | Directory Open Access Journal |
| issn | 2044-6055 |
| language | English |
| last_indexed | 2024-03-08T04:04:48Z |
| publishDate | 2023-04-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj.art-922e69e583c549558ea38b1fad7b7de82024-02-09T07:20:08ZengBMJ Publishing GroupBMJ Open2044-60552023-04-0113410.1136/bmjopen-2022-067899Development of an artificial intelligence system to identify hypoglycaemia via ECG in adults with type 1 diabetes: protocol for data collection under controlled and free-living conditionsMartin O Weickert0Thomas M Barber1Owain Cisuelo2Katy Stokes3Iyabosola B Oronti4Muhammad Salman Haleem5Leandro Pecchia6John Hattersley7Division of Biomedical Sciences, Warwick Medical School, University of Warwick, Coventry, UK2 Endocrinology and Metabolism, University of Warwick, Coventry, UKSchool of Engineering, University of Warwick, Coventry, UKSchool of Engineering, University of Warwick, Coventry, UKSchool of Engineering, University of Warwick, Coventry, UKSchool of Engineering, University of Warwick, Coventry, UKSchool of Engineering, University of Warwick, Coventry, UKSchool of Engineering, University of Warwick, Coventry, UKIntroduction Hypoglycaemia is a harmful potential complication in people with type 1 diabetes mellitus (T1DM) and can be exacerbated in patients receiving treatment, such as insulin therapies, by the very interventions aiming to achieve optimal blood glucose levels. Symptoms can vary greatly, including, but not limited to, trembling, palpitations, sweating, dry mouth, confusion, seizures, coma, brain damage or even death if untreated. A pilot study with healthy (euglycaemic) participants previously demonstrated that hypoglycaemia can be detected non-invasively with artificial intelligence (AI) using physiological signals obtained from wearable sensors. This protocol provides a methodological description of an observational study for obtaining physiological data from people with T1DM. The aim of this work is to further improve the previously developed AI model and validate its performance for glycaemic event detection in people with T1DM. Such a model could be suitable for integrating into a continuous, non-invasive, glucose monitoring system, contributing towards improving surveillance and management of blood glucose for people with diabetes.Methods and analysis This observational study aims to recruit 30 patients with T1DM from a diabetes outpatient clinic at the University Hospital Coventry and Warwickshire for a two-phase study. The first phase involves attending an inpatient protocol for up to 36 hours in a calorimetry room under controlled conditions, followed by a phase of free-living, for up to 3 days, in which participants will go about their normal daily activities unrestricted. Throughout the study, the participants will wear wearable sensors to measure and record physiological signals (eg, ECG and continuous glucose monitor). Data collected will be used to develop and validate an AI model using state-of-the-art deep learning methods.Ethics and dissemination This study has received ethical approval from National Research Ethics Service (ref: 17/NW/0277). The findings will be disseminated via peer-reviewed journals and presented at scientific conferences.Trial registration number NCT05461144.https://bmjopen.bmj.com/content/13/4/e067899.full |
| spellingShingle | Martin O Weickert Thomas M Barber Owain Cisuelo Katy Stokes Iyabosola B Oronti Muhammad Salman Haleem Leandro Pecchia John Hattersley Development of an artificial intelligence system to identify hypoglycaemia via ECG in adults with type 1 diabetes: protocol for data collection under controlled and free-living conditions BMJ Open |
| title | Development of an artificial intelligence system to identify hypoglycaemia via ECG in adults with type 1 diabetes: protocol for data collection under controlled and free-living conditions |
| title_full | Development of an artificial intelligence system to identify hypoglycaemia via ECG in adults with type 1 diabetes: protocol for data collection under controlled and free-living conditions |
| title_fullStr | Development of an artificial intelligence system to identify hypoglycaemia via ECG in adults with type 1 diabetes: protocol for data collection under controlled and free-living conditions |
| title_full_unstemmed | Development of an artificial intelligence system to identify hypoglycaemia via ECG in adults with type 1 diabetes: protocol for data collection under controlled and free-living conditions |
| title_short | Development of an artificial intelligence system to identify hypoglycaemia via ECG in adults with type 1 diabetes: protocol for data collection under controlled and free-living conditions |
| title_sort | development of an artificial intelligence system to identify hypoglycaemia via ecg in adults with type 1 diabetes protocol for data collection under controlled and free living conditions |
| url | https://bmjopen.bmj.com/content/13/4/e067899.full |
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