ECG Signal Visualization Using Machine Learning Models
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Solution Overview
Problem
Current systems for visualizing cardiac signals during ablation procedures are inaccurate and fail to support real-time clinical decision-making, leading to suboptimal treatment outcomes for cardiac arrhythmias like atrial fibrillation.
Innovation Solution
A system and method for visualizing cardiac signals using an ECG machine learning model trained on de-identified medical data, which labels and generates visualization outputs through a graphical user interface, integrating with cloud computing for data storage and scalability, and employing continuous feedback loops for model tuning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional cardiac signal visualization systems are used, then the system complexity is low, but the measurement precision and reliability of cardiac signal visualization are inaccurate
Solution Approach 1:
The system performs preliminary actions by training the machine learning model in advance using large datasets of de-identified medical data before actual cardiac signal visualization. This pre-training enables the model to accurately label and interpret cardiac signals during real-time procedures, achieving high measurement precision without increasing operational complexity during the ablation procedure itself
Solution Approach 2:
The patent introduces an intermediary machine learning model that acts as a mediator between raw cardiac signal data and clinical interpretation. This intermediary layer processes and labels cardiac signals, transforming complex electrical signals into meaningful visualizations that improve measurement precision while managing system complexity through automated intelligence
2Measurement precision
If machine learning models are trained on large datasets, then the measurement precision improves, but the loss of time during data processing and model training increases
Solution Approach 1:
The system performs data processing and model training in advance using historical de-identified medical data before actual clinical use. This preliminary action allows the machine learning model to be fully trained and optimized beforehand, so that during real-time cardiac procedures, the model can quickly label new signals without causing time loss in the critical procedure timeline
Solution Approach 2:
The system maintains continuous useful action by implementing real-time signal processing and labeling during ablation procedures. Once the model is trained, it continuously processes cardiac signals without interruption, providing timely visualizations that support ongoing clinical decision-making without time loss during the procedure
3Reliability
If current visualization systems are used, then the ease of operation is simple, but the reliability for real-time clinical decision-making is insufficient
Solution Approach 1:
The machine learning model performs self-service by automatically labeling and interpreting cardiac signals without requiring manual annotation or complex operator intervention. This automation enhances reliability for clinical decision-making while maintaining ease of operation, as the system independently processes signals and presents visualizations that clinicians can directly use during procedures
Data Source
AI summary
A system for visualization of cardiac signals including at least a processor configured to receive electrocardiogram (ECG) signal data including at least a cardiac signal, label the ECG signal data as a function of an ECG machine learning model, wherein training the ECG machine learning model includes receiving a plurality of de-identified medical data from a medical database, generating ECG training data as a function of the plurality of de-identified medical data, wherein the ECG training data includes the plurality of de-identified medical data correlated to a plurality of signal labels, training the ECG machine learning model as a function of the ECG training data and labeling the ECG signal data as a function of the trained ECG machine learning model, and generate a visualization output as function of the labeled ECG signal data.