AI ECG Strain Evaluation for Low-Cost Consistent Cardiac Assessment
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Solution Overview
Problem
Cardiac strain evaluation is limited by the high cost and inconsistency of echocardiography, making it difficult to measure consistently and affordably.
Innovation Solution
Utilizing electrocardiography data with artificial intelligence to quantify cardiac strain, incorporating ECG embedding vectors and additional information for comprehensive strain evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If echocardiography is used to measure cardiac strain, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces the mechanical/electronic echocardiography system with an AI-based processing system that analyzes ECG data. Instead of using complex echocardiography equipment to directly image and measure cardiac strain, the system uses machine learning models trained on ECG waveforms to infer strain parameters, thereby substituting a simpler input method with a more sophisticated computational approach
Solution Approach 2:
The patent introduces ECG data as an intermediary between the simple electrocardiogram measurement and the complex cardiac strain calculation. The ECG waveform serves as a mediator that contains indirect information about cardiac mechanics, which the AI model then translates into strain measurements without requiring direct mechanical imaging
2Measurement precision
If echocardiography is used to evaluate cardiac strain, then measurement precision is improved, but ease of operation deteriorates due to staff dependency
Solution Approach 1:
The AI-based system performs self-service by automatically analyzing ECG data and generating cardiac strain measurements without requiring skilled operators. The machine learning model independently processes the input ECG waveforms, applies its trained knowledge to calculate strain parameters, and outputs results consistently, eliminating dependency on operator skill level
Solution Approach 2:
The system incorporates feedback mechanisms where the AI model continuously refines its analysis based on the input ECG data characteristics. The model adapts to different ECG waveform patterns and provides consistent strain measurements by feedback-adjusting its processing based on the specific characteristics of each patient's ECG signal
3Measurement precision
If echocardiography is used for cardiac strain measurement, then measurement precision is improved, but loss of time increases due to measurement duration
Solution Approach 1:
The AI model performs preliminary action by pre-processing and analyzing the ECG waveform features before generating the final strain measurement. The system extracts relevant temporal and spatial characteristics from the ECG signal in advance, preparing the data structure needed for rapid strain calculation, which enables fast results without sacrificing precision
Data Source
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AI summary
Embodiments provide an artificial-intelligence-based device and method for evaluating cardiac strain through electrocardiography, the device comprising: an acquisition unit for acquiring electrocardiography data of a subject; an ECG encoder which inputs with the electrocardiography data so as to extract an ECG embedding vector including feature information of the electrocardiography data; and a cardiac strain task processing unit which inputs with the ECG embedding vector so as to include and generate cardiac strain information about one or more task items. Since the electrocardiography data is input into an artificial-intelligence neural network so as to evaluate the degree of cardiac strain, various cardiac strain can be measured and a consistent quantitative assessment can be performed at a low cost.