Cardiac Signal Processing With Machine Learning for Early HCM Detection
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Solution Overview
Problem
Current methods for detecting hypertrophic cardiomyopathy (HCM) are often delayed until a sudden cardiac event occurs, leading to fatal outcomes, and are prone to false positives and negatives, disrupting standard care pathways and causing notification fatigue.
Innovation Solution
A system and method utilizing machine learning algorithms and models, including neural networks, to process ECG data for early detection of HCM, integrating with standard care pathways to provide robust and reliable detection, minimize false positives, and ensure timely follow-up, while reducing data access requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If current methods for detecting HCM are used, then detection can be performed, but detection is delayed until sudden cardiac event occurs leading to fatal outcomes
Solution Approach 1:
The system performs preliminary detection of HCM by analyzing ECG data for subtle indicators before a sudden cardiac event occurs. Machine learning models process ECG signals to identify early signs of hypertrophic cardiomyopathy, enabling proactive identification of at-risk patients and timely intervention before fatal events happen.
2Measurement precision
If current detection methods are used, then detection can be performed, but false positives and negatives occur disrupting standard care pathways
Solution Approach 1:
The system incorporates feedback mechanisms where machine learning models continuously learn from clinical outcomes and expert reviews. Detection results are validated against ground truth data from echocardiography and clinical follow-up, allowing the system to reduce false positives and negatives while maintaining integration with standard care pathways through iterative improvement.
Solution Approach 2:
The patent introduces an intermediary AI-based detection layer between initial ECG screening and definitive diagnostic procedures. This intermediary system pre-screens patients using machine learning analysis of ECG data, filtering out low-risk cases and prioritizing high-risk patients for further evaluation, thereby reducing false positives/negatives while simplifying the overall diagnostic workflow.
3Measurement precision
If more data is collected for HCM detection, then detection accuracy improves, but data access requirements increase
Solution Approach 1:
The system extracts only the most relevant features from ECG data using machine learning dimensionality reduction techniques. Instead of requiring access to large volumes of raw ECG data, the system identifies and processes key temporal and spectral features that are most predictive of HCM, thereby maintaining high detection accuracy while minimizing data access requirements.
Data Source
AI summary
A system for cardiac signal processing, preferably including any or all of: a data collection device, a set of computing and/or processing subsystems, a set of algorithms and/or models, and/or a set of output devices. A method for cardiac signal processing, preferably including processing a set of inputs to determine a set of metrics and determining a set of outputs based on the set of metrics and/or a set of supplementary metrics, and optionally including any or all of: receiving a set of inputs, determining a set of supplementary metrics associated with the set of metrics, and/or triggering the set of outputs.