ECG Cardiac Panels for Multi-Diagnosis Ejection Fraction Tracking
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Solution Overview
Problem
Current systems for tracking cardiac values require extensive human interaction, are inefficient, and produce inaccurate outputs when receiving singular inputs for multiple diagnoses, prone to errors.
Innovation Solution
A system and method utilizing a processor and memory to receive ECG input data, process it through cardiac panels with ECG machine learning models to calculate cardiac values, including probabilities of satisfying ejection fraction level thresholds, and display results via a graphical user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current systems use multiple differing inputs to generate multiple outputs for cardiac tracking, then diagnostic coverage is improved, but system complexity and human supervision requirements increase
Solution Approach 1:
The patent implements a universal ECG machine learning model that can perform multiple cardiac diagnostic functions simultaneously. The model processes ECG data to generate multiple cardiac values (ejection fraction, cardiac arrest risk, cardiomyopathy detection, etc.) from a single input, eliminating the need for multiple separate input systems while maintaining comprehensive diagnostic coverage
Solution Approach 2:
The patent combines multiple diagnostic functions into a single integrated system. The ECG machine learning model merges what were previously separate diagnostic processes into one unified model that outputs multiple cardiac values simultaneously, reducing system complexity while maintaining versatility
2Reliability
If current systems require extensive human interaction and supervision, then diagnostic accuracy can be maintained, but processing efficiency and speed decrease
Solution Approach 1:
The ECG machine learning model performs self-service by automatically processing ECG data and generating cardiac values without requiring human interaction or supervision. The model independently executes diagnostic functions, eliminating the need for manual intervention while maintaining diagnostic accuracy through its trained algorithms
Solution Approach 2:
The patent replaces the mechanical system of human supervision and interaction with an automated machine learning system. The ECG model substitutes human diagnostic processes with algorithm-based analysis, maintaining reliability while dramatically improving processing efficiency and eliminating manual overhead
3Device complexity
If current systems receive singular input to output multiple diagnosis information, then system simplicity is improved, but diagnostic accuracy and error rates worsen
Solution Approach 1:
The ECG machine learning model is designed as a universal system that receives single ECG input and simultaneously performs multiple diagnostic functions with high accuracy. The model's multi-functional architecture enables it to extract multiple cardiac values from one input without sacrificing precision, achieving both simplicity and accuracy
4Productivity
If automated machine learning models are used to reduce human intervention, then processing speed and efficiency improve, but system complexity increases
Solution Approach 1:
The patent segments the automated diagnostic process into distinct functional components within the machine learning model. The ECG model is divided into specialized modules that handle different cardiac value calculations (ejection fraction, arrhythmia detection, etc.), allowing each segment to be optimized independently while working together to achieve high processing speed
Data Source
AI summary
A system for tracking cardiac values including at least a processor configured to receive electrocardiogram (ECG) input data associated with a patient wherein the ECG input data includes ECG signals, input the ECG input data into one or more cardiac panels, wherein each cardiac panel of the one or more cardiac panels is configured to calculate a cardiac value associated with a heart condition and each cardiac panel comprises at least one ECG machine-learning model configured to receive ECG input data and output cardiac values, generate the cardiac values from the cardiac panels as a function of the ECG input data wherein at least one cardiac value of the one or more cardiac values includes a probability of the patient satisfying at least one ejection fraction level threshold and the one or more ECG machine-learning models and display the cardiac values through a graphical user interface.