ECG Ejection Fraction Screening Using Neural Network Estimation
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Solution Overview
Problem
Current methods for measuring cardiac ejection fraction, such as echocardiograms and MRI, are invasive, require specialized personnel, and expensive equipment, limiting widespread screening for asymptomatic ventricular dysfunction (ASVD) which can lead to serious cardiac complications.
Innovation Solution
A computer-based system using electrocardiogram (ECG) data and machine-learning models, particularly neural networks, to estimate ejection fraction from ECG waveforms, enabling widespread and cost-effective screening.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods (echocardiogram, MRI) are used to measure ejection fraction, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces complex mechanical imaging systems (echocardiogram, MRI) with an electrical signal-based system. Instead of using physical scanners and image processing, the invention uses ECG electrical signals combined with machine learning algorithms to estimate ejection fraction, thereby reducing device complexity while maintaining measurement capability
Solution Approach 2:
The patent creates a computational model that copies or simulates the ejection fraction measurement function. Rather than directly measuring with complex hardware, the system uses a machine learning model trained on ECG data to replicate the diagnostic capability, achieving cost-effective estimation without requiring expensive imaging equipment
2Measurement precision
If traditional methods are used to measure ejection fraction, then measurement precision is improved, but loss of time increases due to specialized personnel requirements
Solution Approach 1:
The patent implements a self-service screening system where the ECG-based machine learning model autonomously performs ejection fraction estimation without requiring specialized sonographers or radiologists. The system automatically processes ECG data and provides diagnostic estimates, eliminating time losses associated with specialist involvement while maintaining measurement accuracy
3Device complexity
If ECG-based machine learning model is used to estimate ejection fraction, then device complexity is reduced, but measurement precision may worsen
Solution Approach 1:
The patent transforms the measurement approach by changing from direct physical measurement parameters (imaging data) to electrical signal parameters (ECG waveforms). The machine learning model processes ECG parameters such as QRS complex characteristics, T-wave morphology, and interval measurements to infer ejection fraction, achieving simplified device complexity while maintaining diagnostic precision through sophisticated data processing
4Ease of manufacture
If ECG-based screening is implemented, then cost is reduced, but reliability may worsen due to lack of gold standard validation
Solution Approach 1:
The patent incorporates feedback mechanisms where the machine learning model continuously refines its ejection fraction estimates based on validation against ground truth data. The system uses feedback from training data and performance metrics to improve its accuracy, ensuring reliable diagnostic estimates while maintaining cost-effectiveness through automated processing and reduced need for expensive follow-up tests
Data Source
AI summary
Systems, methods, devices, and techniques for estimating a heart disease prediction of a mammal. An electrocardiogram (ECG) procedure is performed on a mammal, and a computer system obtains ECG data that describes results of the ECG over a period of time. The system provides a predictive input that is based on the ECG data to a predictive model, such as a neural network or other machine-learning model. In response, the predictive model processes the input to generate an estimated heart disease predictive characteristic of the mammal. The system outputs the estimated heart disease prediction of the mammal for presentation to a user.