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Non-invasive detection of hypoglycaemia in people with diabetes using brain wave activity

Project Member(s): Nguyen, T., Nguyen, H.

Funding or Partner Organisation: National Health & Medical Research Council (NHMRC Project Grants)

Start year: 2016

Summary: Diabetes affects more than 1.14 million Australians who registered with the National Diabetes Services Scheme in 2014, and among those, 31% required insulin therapy. Hypoglycaemia (low blood glucose) is a feared and common unwanted effect of diabetes treated with insulin therapy, and is the main reason why these people often fail to achieve the levels of glycaemic control necessary to prevent diabetic complications. Hypoglycaemia is a problem because the brain depends on a constant supply of glucose to maintain its function, and severe hypoglycaemia causes acute brain malfunction that leads to coma and is sometimes life-threatening. Current treatments for nocturnal hypoglycaemia are usually ineffective. Over the last five years, our research team has been influential in leading several major developments in the field of non-invasive hypoglycaemia detection. In the proposed study, we aim to identify the surface electroencephalographic changes that predict an episode of hypoglycaemia. The research proposed in this project utilises a wide range of cutting edge technologies and draws together many different scientific approaches including biomedical engineering, neuroscience and artificial intelligence. The technology will reduce healthcare costs and has the potential to generate new medical devices with major export prospects.


Ling, SH, San, PP & Nguyen, HT 2016, 'Non-invasive hypoglycemia monitoring system using extreme learning machine for Type 1 diabetes', ISA TRANSACTIONS, vol. 64, pp. 440-446.
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San, PP, Ling, SH & Nguyen, HT 1970, 'Deep learning framework for detection of hypoglycemic episodes in children with type 1 diabetes', 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, Orlando, pp. 3503-3506.
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Keywords: diabetic complications, hypoglycaemia, EEG

FOR Codes: Biomedical Engineering, Diabetes, Clinical health