A novel algorithm for prediction and detection of hypoglycemia based on continuous glucose monitoring and heart rate variability in patients with type 1 diabetes
Open accessAbstract
Hypoglycemia is a common and serious side effect of insulin therapy in patients with diabetes. Early detection and prediction of hypoglycemia may improve treatment and avoidance of serious complications. Continuous glucose monitoring (CGM) has previously been used for detection of hypoglycemia, but with a modest accuracy. Therefore, our aim was to investigate whether a novel algorithm that adds information of the complex dynamic/pattern of heart rate variability (HRV) could improve the accuracy of hypoglycemia as detected by a CGM device. Data from 10 patients with type 1 diabetes studied during insulin-induced hypoglycemia were obtained. Blood glucose samples were used as reference. HRV patterns and CGM data were combined in a mathematical prediction algorithm. Detection of hypoglycemic periods, performed by the algorithm, was treated as a pattern recognition problem and features/patterns derived from HRV and CGM prior to each blood glucose sample were used to decide if that particular point in time was below the hypoglycemic threshold of 3.9 mmol/L. A total of 903 samples were analyzed by the novel algorithm, which yielded a sensitivity of 79% and a specificity of 99%. The algorithm was able to detect 16/16 hypoglycemic events with no false positives and had a lead time of 22 minutes as compared to the CGM device. Detection accuracy and lead time were significantly improved by the novel algorithm compared to that of CGM alone.
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ML-based approaches using wearable data could improve hypoglycemia prediction in CGM and closed-loop users, as shown previously.
“In CGM and closed-loop users, ML-based approaches using wearable data could improve hypoglycemia prediction, as shown previously (12).”
From Noninvasive Hypoglycemia Detection in People With Diabetes Using Smartwatch Data (2023) · cites this paper as [12]
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- Authors
- Cichosz, S. L., Frystyk, J., Hejlesen, O. K., Tarnow, L., & Fleischer, J.
- Affiliations
- Aarhus University Hospital (DK)
- Aalborg University (DK)
- Aarhus University (DK)
- Venue
- Journal of Diabetes Science and Technology
- Publisher
- SAGE Publications
- Published
- 2014-03-31
- Volume
- 8
- Issue
- 4
- Pages
- 731-737
- ISSN
- 1932-2968 (ISSN-L)
- PMC
- PMC4764234
- OpenAlex
- W2134508273
- Cited by
- 88
- References
- 39
- Citations by year
- 2026: 4 · 2025: 4 · 2024: 10 · 2023: 9 · 2022: 10 · 2021: 14 · 2020: 5 · 2019: 8 · 2018: 5 · 2017: 8 · 2016: 6 · 2015: 4 · 2014: 1
- FWCI
- 5.09
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Diabetes Science and Technology (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- VBN Forskningsportal (Aalborg Universitet) (submittedVersion, other-oa)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- hypoglycemia prediction, hypoglycemia detection, type 1 diabetes, continuous glucose monitoring, heart rate variability, insulin-induced hypoglycemia, pattern recognition algorithms, lead time, insulin therapy
- MeSH terms
- Adult, Algorithms, Diabetes Mellitus, Type 1, Electrocardiography, Heart Rate, Humans, Hypoglycemia, Hypoglycemic Agents, Insulin, Male, Middle Aged, Predictive Value of Tests, Blood Glucose Self-Monitoring, Models, Statistical
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