Non-invasive hypoglycemia monitoring system using extreme learning machine for Type 1 diabetes

Hypoglycemia is a very common in type 1 diabetic persons and can occur at any age. It is always threatening to the well-being of patients with Type 1 diabetes mellitus (T1DM) since hypoglycemia leads to seizures or loss of consciousness and the possible development of permanent brain dysfunction under certain circumstances. Because of that, an accurate continuing hypoglycemia monitoring system is a very important medical device for diabetic patients. In this paper, we proposed a non-invasive hypoglycemia monitoring system using the physiological parameters of electrocardiography (ECG) signal. To enhance the detection accuracy, extreme learning machine (ELM) is developed to recognize the presence of hypoglycemia. A clinical study of 16 children with T1DM is given to illustrate the good performance of ELM. •Extreme learning machine (ELM) is developed to recognize the presence of hypoglycemic episodes.•A non-invasive hypoglycemic monitoring system based on ECG signals is developed.•A real experiment with Type 1 diabetes patients is given to show the performance of the proposed method.