Abstract: Disclosed are methods, circuits, devices, systems and functionally associated machine executable code for glucose event detection. A system for glucose event detection includes a recursive neural network (RNN) model for generating, for a monitored subject, blood glucose level (BGL) output streams for respective, system fed, heart beats per minute (BPM) input streams of a monitored subject. A supervised training mechanism, for training the artificial recurrent neural network (RNN) model, compares model generated blood glucose level (BGL) output streams to time-aligned blood glucose level (BGL) output streams from a continuous glucose monitoring (CGM) device concurrently monitoring the same subject.

Abstract: Disclosed are methods, circuits, devices, systems and functionally associated machine executable code for glucose monitoring, analysis and remedy. A subject glucose level baseline is calculated based on monitored subject glucose level readings collected by a non-invasive sensor assembly and subject mobile device sensors data. Newly received, monitored subject glucose level readings sets are compared to subject glucose level baseline values to detect anomalies Indications of a glucose level anomaly, are analyzed by reference of one or more subject behavioral or physiological conditions concurrent with the anomaly. Monitored subject behavioral and physiological conditions are analyzed to determine a representation of the subject over a multi-condition diabetic risk graph/map, subject feedback is generated based on the representation.