ECG-Based Potassium Monitoring via Machine Learning
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Solution Overview
Problem
Current methods for monitoring electrolyte levels, such as potassium, in the body are invasive and lack the accuracy needed for individualized monitoring, particularly in detecting subtle deviations that can lead to serious health complications.
Innovation Solution
A non-invasive system that utilizes an electrocardiogram (ECG) sensor to collect data, which is then analyzed using a machine learning model trained on historical ECG data and analyte levels to automatically determine potassium levels and display them on a user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If invasive blood sampling is used to monitor electrolyte levels, then measurement accuracy is improved, but patient comfort and ease of operation deteriorate
Solution Approach 1:
The patent replaces the mechanical/invasive blood sampling system with an electrocardiogram-based measurement system. The ECG sensor non-invasively captures electrical signals from the heart, which are then processed through machine learning models to derive electrolyte levels, eliminating the need for needle insertion and blood draws while maintaining monitoring capability
Solution Approach 2:
The patent introduces ECG signals as an intermediary medium to indirectly measure electrolyte levels. Instead of directly analyzing blood, the system uses the heart's electrical activity as a proxy, which reflects electrolyte concentrations through their influence on cardiac electrophysiology, enabling non-invasive measurement
2Loss of time
If continuous monitoring is implemented, then detection timeliness is improved, but device complexity increases
Solution Approach 1:
The patent makes the ECG sensor multi-functional by enabling it to serve both traditional cardiac monitoring and electrolyte level detection. The same sensor hardware and signal processing infrastructure are used for both purposes, eliminating the need for separate dedicated monitoring devices and reducing overall system complexity despite continuous monitoring capabilities
Solution Approach 2:
The machine learning model is trained on historical ECG data and analyte levels to enable automatic electrolyte level determination from routine ECG signals. The system uses existing cardiac monitoring data for dual purposes, allowing continuous electrolyte monitoring without requiring additional sensors or increasing hardware complexity
Data Source
AI summary
Disclosed are systems for non-invasively determining a measurement of an analyte. The systems include an electrocardiogram sensor and a processing device operatively coupled to the electrocardiogram sensor. The processing device can execute instructions to receive electrocardiogram data from the electrocardiogram sensor and apply a machine learning model, wherein the machine learning model has been trained based on previous electrocardiogram data associated with a subject and source of an analyte measurement associated with the subject. The system may also determine an indication of a level of the analyte based on the electrocardiogram data.