Deep learning framework for detection of hypoglycemic episodes in children with type 1 diabetes
Abstract
Most Type 1 diabetes mellitus (T1DM) patients have hypoglycemia problem. Low blood glucose, also known as hypoglycemia, can be a dangerous and can result in unconsciousness, seizures and even death. In recent studies, heart rate (HR) and correct QT interval (QTc) of the electrocardiogram (ECG) signal are found as the most common physiological parameters to be effected from hypoglycemic reaction. In this paper, a state-of-the-art intelligent technology namely deep belief network (DBN) is developed as an intelligent diagnostics system to recognize the onset of hypoglycemia. The proposed DBN provides a superior classification performance with feature transformation on either processed or un-processed data. To illustrate the effectiveness of the proposed hypoglycemia detection system, 15 children with Type 1 diabetes were volunteered overnight. Comparing with several existing methodologies, the experimental results showed that the proposed DBN outperformed and achieved better classification performance.
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Our approach uses consumer-grade smartwatches without additional inputs (e.g., insulin, meals), unlike previously proposed approaches detecting hypoglycemia from wearables.
“In contrast to previously proposed approaches detecting hypoglycemia from wearables (9–11), our approach uses consumer-grade smartwatches without additional inputs (e.g., insulin, meals), increasing usability.”
From Noninvasive Hypoglycemia Detection in People With Diabetes Using Smartwatch Data (2023) · cites this paper as [11]
Cites
Epidemiology of severe hypoglycemia in the diabetes control and complications trial
(1991) · The American Journal of Medicine · doi:10.1016/0002-9343(91)80085-z · Cited by 807 · see citing works
(1995) · Diabetes Care · doi:10.2337/diacare.18.11.1415 · Cited by 305 · see citing works
Learning deep physiological models of affect
Martinez, H. P., Bengio, Y., & Yannakakis, G. N. (2013) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2013.2247823 · Cited by 287 · see citing works
Current development in non-invasive glucose monitoring
Ferrante do Amaral, C. E., & Wolf, B. (2008) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2007.06.003 · Cited by 269 · see citing works
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Artificial intelligence for diabetes management and decision support: Literature review
Contreras, I., & Vehi, J. (2018) · Journal of Medical Internet Research · doi:10.2196/10775 · Cited by 560 · see citing works
Noninvasive Hypoglycemia Detection in People With Diabetes Using Smartwatch Data
Lehmann, V., Föll, S., Maritsch, M., van Weenen, E., Kraus, M., Lagger, S., Odermatt, K., Albrecht, C., Fleisch, E., Zueger, T., et al. (2023) · Diabetes Care · doi:10.2337/dc22-2290 · Cited by 47 · see citing works
- Authors
- San, P. P., Ling, S. H., & Nguyen, H. T.
- Affiliations
- Institute for Infocomm Research (SG)
- University of Technology Sydney (AU)
- Venue
- 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
- Publisher
- IEEE
- Published
- 2016-08
- Volume
- 2016
- Pages
- 3503-3506
- ISSN
- 2375-7477 (ISSN-L)
- OpenAlex
- W2532385626
- Cited by
- 42
- References
- 4
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 6 · 2023: 4 · 2022: 8 · 2021: 3 · 2020: 8 · 2019: 6 · 2018: 3 · 2017: 1
- FWCI
- 9.33
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (publishedVersion)
- PubMed (publishedVersion)
- UTS ePRESS (University of Technology Sydney) (submittedVersion)
- Swinburne figshare (Swinburne University of Technology) (submittedVersion)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- hypoglycemia detection, type 1 diabetes, children, deep belief network, electrocardiogram, heart rate, QTc interval, physiological signal classification
- MeSH terms
- Algorithms, Blood Glucose, Child, Diabetes Mellitus, Type 1, Electrocardiography, Heart Rate, Humans, Hypoglycemia, Signal Processing, Computer-Assisted, Neural Networks, Computer
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