Exercise ECG Phase Analysis for CAD and MACE Prediction
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Solution Overview
Problem
Current methods for detecting coronary artery disease (CAD) and predicting major adverse cardiovascular events (MACE) using exercise ECG tests lack sensitivity and specificity, leading to underutilization of recorded data and potential false positives/negatives, while existing AI tools for resting ECGs are not sufficiently accurate or cost-effective.
Innovation Solution
Utilizing machine learning algorithms to analyze detailed data from exercise ECG tests, including multiple segments and phases, to enhance detection and prediction of CAD and MACE, improving sensitivity, specificity, and reducing variability through diverse signal pattern exposure and robust assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional stress ECG tests are used for CAD detection, then the test is simple and affordable, but the sensitivity and specificity are limited leading to false positives and negatives
Solution Approach 1:
The patent segments the ECG signal into multiple phases (rest, exercise, recovery) and further divides each phase into multiple segments. Machine learning models then analyze these segmented portions to detect patterns that would be missed in traditional holistic analysis, improving sensitivity and specificity while maintaining the simplicity of the ECG test itself
Solution Approach 2:
The patent introduces machine learning algorithms as an intermediary between the ECG signal and diagnostic interpretation. The ML models process the raw ECG data, identify complex patterns, and provide enhanced diagnostic accuracy without requiring complex hardware or invasive procedures, thus maintaining affordability while improving precision
2Measurement precision
If AI analysis tools are applied to resting ECG tests, then detection capability is enhanced, but the tools are not sufficiently accurate or cost-effective for routine use
Solution Approach 1:
The patent changes the input parameters from single-phase resting ECG to multi-phase ECG data (rest, exercise, recovery). This parameter transformation enables machine learning models to capture dynamic cardiac responses and identify patterns that are not visible in resting ECG alone, significantly improving detection accuracy and reliability for routine clinical use
3Measurement precision
If exercise ECG test data is fully utilized, then detection accuracy improves, but the complexity of data processing increases
Solution Approach 1:
The patent applies segmentation to divide the comprehensive EET data into manageable phases and segments, making the complex data structure more manageable for machine learning processing. This segmentation enables systematic analysis of different physiological states without overwhelming computational complexity
Solution Approach 2:
The machine learning models automatically process and interpret the ECG data, performing feature extraction, pattern recognition, and diagnostic assessment without requiring manual intervention. This automation handles the complexity of full data utilization while maintaining high detection accuracy
Data Source
AI summary
Provided herein are computer implemented methods and systems for prediction of major adverse cardiovascular events (MACE) and/or detection of CAD in a patient, based on phases of exercise electrocardiogram (ECG) test (EET) data.