EEG-Based Hypoglycemia Detection and Glucose Management
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Solution Overview
Problem
Current methods for diagnosing and managing metabolic diseases, particularly diabetes, face challenges in accurately detecting and managing hypoglycaemia, especially in patients with Type 1 diabetes undergoing intensive insulin therapy, where counter-regulatory responses are deficient, leading to potentially life-threatening complications such as coma and convulsions.
Innovation Solution
A method and system utilizing electroencephalography (EEG) signal analysis to detect shifts in alpha, theta, and delta wave frequencies, combined with a Bayesian neural network, to identify hypoglycaemia and automatically administer glucose or glycogen doses through a processing system, potentially integrated into an artificial pancreas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional blood glucose monitoring methods are used, then direct measurement of glucose levels is achieved, but the method is invasive and requires patient intervention
Solution Approach 1:
The patent replaces mechanical/invasive blood glucose monitoring with non-invasive EEG signal detection. The system uses electroencephalography to measure brain wave patterns that correlate with glucose levels, eliminating the need for physical blood draws or sensor insertions while maintaining detection capability through neurological signal analysis
Solution Approach 2:
The patent introduces EEG signals as an intermediary indicator to indirectly measure glucose levels. Instead of directly measuring glucose in the blood, the system detects changes in brain wave patterns (alpha, theta, delta waves) that serve as a proxy marker for glucose status, enabling non-invasive monitoring through the intermediary relationship between brain waves and metabolic state
2Reliability
If frequent blood glucose testing is performed to manage hypoglycaemia, then glucose control is improved, but patient burden and discomfort increase
Solution Approach 1:
The patent enables the system to autonomously monitor glucose levels through continuous EEG signal acquisition and analysis without requiring active patient participation. The automated detection system processes brain wave patterns independently, eliminating the need for patients to perform manual blood glucose tests while maintaining reliable glucose control monitoring
Solution Approach 2:
The patent implements continuous monitoring through ongoing EEG signal collection and real-time analysis. Unlike intermittent manual testing, the system continuously tracks brain wave patterns to detect glucose changes, providing uninterrupted surveillance that improves management accuracy while reducing the discrete burden of repeated patient interventions
3Reliability
If counter-regulatory responses are enhanced to prevent hypoglycaemia, then glucose safety is improved, but the complexity of metabolic regulation increases
Solution Approach 1:
The patent implements a feedback mechanism where EEG signal analysis continuously monitors glucose status and provides information to trigger appropriate responses. The system detects changes in brain wave patterns, compares them against established thresholds, and activates counter-regulatory mechanisms when hypoglycaemia is detected, creating a closed-loop control system that improves safety through automated feedback
Solution Approach 2:
The patent changes the monitoring parameter from direct blood glucose concentration to EEG signal characteristics (frequency, amplitude, wave patterns). By transforming the measurement approach into neurological parameters, the system simplifies the detection mechanism while maintaining the ability to trigger appropriate metabolic responses, reducing the complexity of the overall regulation system
Data Source
AI summary
This invention describes a method for determining an abnormality of metabolic function and/or a variation of metabolic function, the method including the steps of, in a processing system receiving electroencephalography (EEG) signal information (100), analyzing the signal information (110), and determining the abnormality from the analysis (120). It also describes a method for managing the variation accordingly, the method including the steps of, detecting the abnormality (400), applying patient information (410), determining appropriate dose (420), and providing the required dose (430).