Clinical evaluation of a noninvasive alarm system for nocturnal hypoglycemia.

Background: The aim of this study was to evaluate the performance of a prototype noninvasive alarm system (HypoMon) for the detection of nocturnal hypoglycemia. A prospective cohort study evaluated an alarm system that included a sensor belt, a radio frequency transmitter for chest belt signals, and a receiver. The receiver incorporated integrated "real-time" algorithms designed to recognize hypoglycemia "signatures" in the physiological parameters monitored by the sensor belt.

Hypoglycemia alarm enhancement using data fusion.

Background: The acceptance of closed-loop blood glucose (BG) control using continuous glucose monitoring systems (CGMS) is likely to improve with enhanced performance of their integral hypoglycemia alarms. This article presents an in silico analysis (based on clinical data) of a modeled CGMS alarm system with trained thresholds on type 1 diabetes mellitus (T1DM) patients that is augmented by sensor fusion from a prototype hypoglycemia alarm system (HypoMon). This prototype alarm system is based on largely independent autonomic nervous system (ANS) response features.

Real-time detection of nocturnal hypoglycemic episodes using a novel non-invasive hypoglycemia monitor.

Hypoglycemia or low blood glucose is a common and serious side effect of insulin therapy in patients with diabetes. Hypoglycemia is unpleasant and can result in unconsciousness, seizures and even death. HypoMon is a realtime non-invasive monitor that measures relevant physiological parameters continuously to provide detection of hypoglycemic episodes in Type 1 diabetes mellitus patients (T1DM).

Detection of nocturnal hypoglycemic episodes (natural occurrence) in children with Type 1 diabetes using an optimal Bayesian neural network algorithm.

Hypoglycemia or low blood glucose is dangerous and can result in unconsciousness, seizures and even death. It is a common and serious side effect of insulin therapy in patients with diabetes. HypoMon is a non-invasive monitor that measures some physiological parameters continuously to provide detection of hypoglycemic episodes in Type 1 diabetes mellitus patients (T1DM).

Detection of hypoglycemic episodes in children with type 1 diabetes using an optimal Bayesian neural network algorithm.

Hypoglycemia or low blood glucose is a common and serious side effect of insulin therapy in patients with diabetes. HypoMon is a non-invasive monitor that measures some physiological parameters continuously to provide detection of hypoglycemic episodes in Type 1 diabetes mellitus patients (T1DM). Based on heart rate, corrected QT interval of the ECG signal and skin impedance, a Bayesian neural network detection algorithm has been developed to recognize the presence of hypoglycemic episodes.

Neural-network detection of hypoglycemic episodes in children with type 1 diabetes using physiological parameters.

The most common and highly feared adverse effect of intensive insulin therapy in patients with diabetes is the increased risk of hypoglycemia. Symptoms of hypoglycemia arise from the activation of the autonomous central nervous systems and from reduced cerebral glucose consumption. HypoMon is a non-invasive monitor that measures some physiological parameters continuously to provide detection of hypoglycemic episodes in Type 1 diabetes mellitus patients (T1DM).