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An LSTM-based Approach Towards Automated Meal Detection from Continuous Glucose Monitoring in Type 1 Diabetes Mellitus | IEEE Conference Publication | IEEE Xplore

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Type 1 Diabetes Mellitus (T1DM) is a metabolic disorder that results from a chronic autoimmune destruction of the insulin-producing pancreatic beta cells, and is characterized by elevated blood glucose levels. Long-term hyperglycaemia due to the absence of insulin secretion is associated with the onset of macrovascular (coronary artery disease, peripheral arterial disease, and stroke) and microvascular (diabetic nephropathy, neuropathy, and retinopathy) complications [1]. The injurious effects of hyperglycaemia can be prevented through optimal glycaemic control, which involves regular glucose measurements and exogenous insulin administration [2]. Technological advances in glucose sensors and insulin pumps, along with algorithmic progress towards the automated estimation of appropriate insulin infusion rates, have brought forward the development of wearable Artificial Pancreas (AP), with the ultimate goal to enable effective T1DM management [3]. Despite the promising performance of clos

Type 1 Diabetes Mellitus (T1DM) is a metabolic disorder that results from a chronic autoimmune destruction of the insulin-producing pancreatic beta cells, and is characterized by elevated blood glucose levels. Long-term hyperglycaemia due to the absence of insulin secretion is associated with the onset of macrovascular (coronary artery disease, peripheral arterial disease, and stroke) and microvascular (diabetic nephropathy, neuropathy, and retinopathy) complications [1]. The injurious effects of hyperglycaemia can be prevented through optimal glycaemic control, which involves regular glucose

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