Hypoglycemia Prediction System Using Multi-Parameter Physiological Monitoring
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Solution Overview
Problem
Individuals with type 1 diabetes face challenges in managing hypoglycemia, a condition that can lead to severe complications and death due to undetected low blood glucose levels, especially at night when autonomic responses are reduced, and existing technologies do not adequately address the risk of sudden arrhythmic death associated with hypoglycemia.
Innovation Solution
A computer system that monitors user data including food intake, glucose readings, medication, and physiological parameters like heart rate variability, comparing them to predetermined values to generate real-time alerts for hypoglycemia risks, transmitting alerts to users or medical practitioners, and continuing measurement when values are below thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If continuous real-time monitoring of physiological parameters is implemented, then detection reliability of hypoglycemia is improved, but device complexity and energy consumption increase
Solution Approach 1:
The system divides monitoring into multiple physiological parameter segments (heart rate, heart rate variability, oxygen saturation, temperature, galvanic skin response) rather than monitoring a single parameter. Each parameter provides complementary information about hypoglycemia risk, improving detection reliability while allowing the system to function with multiple independent measurement channels that can operate autonomously
Solution Approach 2:
The system uses physiological parameters as intermediary indicators rather than directly measuring blood glucose levels. These physiological measurements serve as mediators that reflect metabolic state changes associated with hypoglycemia, enabling indirect detection that reduces the complexity of direct glucose monitoring while maintaining detection reliability
2Measurement precision
If multiple physiological parameters are monitored simultaneously, then prediction accuracy of hypoglycemia is improved, but data processing complexity increases
Solution Approach 1:
The system monitors multiple physiological parameters beyond what a single-parameter system would use, applying partial analysis to each parameter individually before integrating results. This allows the system to process data in manageable portions rather than attempting to analyze all parameters simultaneously, reducing overall processing complexity while maintaining prediction accuracy through cumulative evidence
Solution Approach 2:
The system transitions from analyzing single-parameter data to multi-dimensional physiological space by incorporating heart rate, heart rate variability, oxygen saturation, temperature, and galvanic skin response. This dimensional expansion improves prediction accuracy by capturing hypoglycemia manifestations across different physiological domains, while each dimension can be processed independently before integration
3Loss of time
If real-time alerts are generated and transmitted to multiple recipients, then response time to hypoglycemia is improved, but system resource consumption increases
Solution Approach 1:
The system pre-configures alert recipient lists and notification channels during system setup, storing this information for later use. When hypoglycemia is detected, the system can immediately transmit alerts to multiple pre-designated recipients (user, family members, medical practitioners) without requiring real-time decision-making about notification routing, thus reducing response time while minimizing additional processing overhead
Solution Approach 2:
The system automatically generates and transmits alerts without requiring manual intervention from the user or other parties. The alert transmission mechanism operates autonomously, selecting recipients and sending notifications based on pre-configured parameters, which reduces the energy overhead associated with manual alert management and ensures rapid response to hypoglycemic events
Data Source
AI summary
The present invention provides for a computer system that includes at least one server having software stored on a non-transient computer readable medium; where, upon execution of the software, the at least one server is at least configured to: i) receiving, in real-time, an input from a user; ii) receiving, in real-time, physiological data representative of a physiological measurement of physiological characteristic of the user; iii) comparing, in real-time, the physiological measurement of the user to a pre-determined physiological value associated with the physiological characteristic retrieved from a database; iv) based on the comparing, determining, in real-time, that a difference between the physiological measurement of the user and the pre-determined physiological value is higher or smaller than the predetermined threshold value, and then: v) generating, in real-time, at least one alert; vi) transmitting, in real-time, the at least one alert.