Generative Model Simulates Echocardiograms from ECG Signals
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Solution Overview
Problem
Echocardiograms are less frequently performed due to their higher cost compared to electrocardiograms, limiting their diagnostic availability for heart diseases.
Innovation Solution
A system and method that utilize a processor and a non-transitory computer executable storage medium to generate simulated echocardiogram data from electrocardiogram (ECG) images and signals, training a generative model with ECG data to produce echocardiogram-like outputs, thereby reducing the need for direct echocardiogram procedures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If echocardiograms are performed frequently for diagnosis, then diagnostic accuracy and availability improve, but cost increases
Solution Approach 1:
The patent creates a simulated echocardiogram that copies the essential diagnostic features of a real echocardiogram using ECG data as input. The generative model produces a synthetic echocardiogram image that mimics the visual characteristics and diagnostic information of actual echocardiogram images, allowing diagnosis without performing the expensive actual procedure
Solution Approach 2:
The patent replaces the expensive echocardiogram procedure with a cheaper alternative - using readily available ECG data that can be obtained through simple, low-cost electrocardiogram monitoring. The ECG data serves as a disposable, inexpensive input that generates the needed diagnostic information without requiring costly echocardiogram equipment and procedures
2Quantity of substance
If echocardiograms are performed less frequently to reduce cost, then cost decreases, but diagnostic availability and frequency are limited
Solution Approach 1:
The patent makes the ECG system multi-functional by enabling it to not only record electrical heart activity but also to generate simulated echocardiogram images. This universal approach allows a single, low-cost ECG device to perform multiple diagnostic functions that previously required separate, expensive echocardiogram equipment
Solution Approach 2:
The simulated echocardiogram creates a visual copy of what a real echocardiogram would show, enabling frequent diagnostic imaging through low-cost ECG data processing. This copying mechanism removes the barrier of expensive equipment while maintaining diagnostic capability
3Measurement precision
If generative model training uses extensive ECG data, then model accuracy improves, but training time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing and organizing ECG data before training, and by using a generative model architecture that is pre-configured for efficient training. The system prepares the training data in advance and uses preliminary model structures that accelerate the training process while maintaining accuracy
Solution Approach 2:
The patent optimizes training parameters such as learning rate, batch size, and model architecture parameters to achieve faster convergence. By carefully tuning these parameters, the system reduces training time while maintaining or improving the accuracy of the generated echocardiograms
Data Source
AI summary
A system for generating imaging information based on at least a signal includes at least a processor and a memory communicatively connected thereto, where the memory contains instructions configuring the at least a processor to receive a plurality of unconditioned electrocardiogram images, a plurality of conditioned electrocardiogram images and a corpus of ECG signals. Additionally, the processor learns at least a discrepancy between the plurality of unconditioned electrocardiogram images and the plurality of conditioned electrocardiogram images using a self-supervised machine learning model. Further, the processor generates a generative model as a function of the at least a discrepancy wherein generating the generative model comprises training the generative model using generative training data wherein the generative training data correlates at least an ECG signal from the corpus of ECG signals input to an output echocardiogram datum and output a diagnosis as a function of the output echocardiogram datum.