Hyperkalemia Prediction via ECG Neural Network
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Solution Overview
Problem
Current methods for managing hyperkalemia, such as blood sampling, are invasive and inconvenient, making it difficult to constantly monitor potassium levels in patients with related diseases like diabetes and chronic kidney disease, which can lead to fatal cardiac arrhythmias.
Innovation Solution
A system and method for constructing a neural network model using electrocardiogram data to predict hyperkalemia, involving data collection, processing, and generation of a training dataset to classify ECG data into normal and abnormal states, enabling non-invasive diagnosis through a smart band that applies the neural network model to user-collected ECG data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If blood sampling is used to measure potassium concentration, then measurement precision is improved, but ease of operation deteriorates and loss of time increases
Solution Approach 1:
The patent replaces the mechanical/invasive blood sampling method with an electrical measurement system. Specifically, it uses electrocardiogram (ECG) signals processed through a neural network model to predict potassium concentration, substituting the physical blood draw with an electrical signal-based diagnostic approach that is non-invasive and can be performed continuously.
Solution Approach 2:
The patent introduces an intermediary system between the patient's body and the measurement process. Instead of directly measuring potassium through blood sampling, it uses ECG signals as an intermediary that reflects potassium levels indirectly. The neural network model acts as a mediator that translates ECG signal characteristics into potassium concentration predictions.
2Measurement precision
If blood sampling is used to measure potassium concentration, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent enables continuous monitoring of potassium levels through continuous ECG signal acquisition and processing. The system can continuously analyze ECG signals to predict potassium concentration in real-time, eliminating the need for periodic hospital visits and providing uninterrupted monitoring of the patient's electrolyte status.
Solution Approach 2:
The patent replaces the time-consuming blood sampling process with rapid electrical signal processing. The neural network model can process ECG signals instantaneously to provide potassium concentration predictions, dramatically reducing the time required for measurement compared to traditional blood sampling and laboratory analysis.
3Ease of operation
If neural network model is constructed using ECG data, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The patent performs preliminary work by pre-training the neural network model using a large dataset of ECG signals and corresponding potassium concentration measurements. This pre-training phase is conducted in advance, allowing the model to be ready for deployment without requiring complex real-time training. The model learns to recognize patterns associated with different potassium levels during this preliminary stage.
Solution Approach 2:
The patent uses a simplified version or copy of the complex neural network architecture that has been pre-trained and optimized. Instead of implementing the full complexity of the training process in the diagnostic device, it deploys a pre-trained model that can be applied directly to new ECG data, reducing the computational complexity required at the point of use while maintaining high diagnostic accuracy.
Data Source
AI summary
The present invention relates to a system for constructing a hyperkalemia prediction algorithm through an electrocardiogram, a method for constructing the hyperkalemia prediction algorithm through the electrocardiogram by using the same, and a hyperkalemia prediction system using the electrocardiogram, and the system for constructing a hyperkalemia prediction algorithm through an electrocardiogram includes: a data collection unit collecting electrocardiogram data of multiple hyperkalemia patients; a data processing unit generating a training dataset for machine learning based on the electrocardiogram data collected by the data collection unit; and a model generation unit constructing a neural network model for predicting a hyperkalemia using an electrocardiogram based on the training dataset provided by the data processing unit.