Vector Velocity Field Analysis for Atrial Fibrillation Source Classification
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Solution Overview
Problem
Current methods fail to accurately differentiate between active and passive focal sources in atrial fibrillation (aFib) using electrocardiogram (ECG) signals, and are unable to determine the successful termination of aFib from ECG signals, limiting their effectiveness in catheter ablation procedures.
Innovation Solution
A machine learning and artificial intelligence system that models a vector velocity field from ECG data, classifies focal and rotor indications using kernels, and distinguishes between active and passive focal sources, enabling the detection of aFib termination by analyzing local activation times and electrical wave directions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional mechanisms use velocity characteristics of aFib sources determined by electrographic flow mapping, then some velocity information can be obtained, but the mechanisms fail to differentiate between active and passive focal sources
Solution Approach 1:
The patent introduces an intermediary computational layer (velocity vector field analysis and machine learning classification) between the raw electrographic flow mapping data and the final differentiation of active/passive focal sources. This intermediary processing transforms the insufficient velocity information into a comprehensive classification system that can distinguish between active and passive focal sources with high accuracy.
Solution Approach 2:
The patent replaces conventional electrographic flow mapping mechanisms with a machine learning-based detection system that processes velocity vector field data. This substitution transforms the limited conventional approach into an intelligent system capable of automated classification and accurate differentiation of focal source types.
2Reliability
If physicians use conventional mechanisms to analyze ECG signals, then basic velocity characteristics can be determined, but the ability to determine successful aFib termination is lost
Solution Approach 1:
The detection system performs self-service by automatically analyzing ECG signals, determining velocity characteristics, classifying focal sources, and identifying aFib termination without requiring physician interpretation. The system serves itself by integrating all detection and classification functions into an automated pipeline that reliably determines treatment success.
Solution Approach 2:
The patent implements feedback mechanisms where the detection system continuously monitors ECG signals during and after ablation procedures, providing real-time information about aFib activity and termination status. This feedback loop enables reliable determination of successful treatment by comparing pre- and post-ablation velocity field characteristics.
3Measurement precision
If phase mapping and vector analysis are used without machine learning algorithms, then basic flow mapping can be performed, but detailed classification of focal sources and termination detection fail
Solution Approach 1:
The patent applies preliminary action by first computing the velocity vector field from phase mapping data before applying machine learning classification. This preliminary computation of velocity characteristics prepares the data in a form that enables subsequent accurate classification of focal sources, transforming raw phase information into meaningful velocity-based features.
Solution Approach 2:
The detection system combines multiple analytical approaches (phase mapping, velocity vector field analysis, machine learning classification) into a composite detection methodology. This composite approach integrates the strengths of conventional techniques with the power of machine learning to achieve precise focal source classification and termination detection that neither method could achieve alone.
Data Source
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AI summary
A method is provided. The method is implemented by a detection engine embodied in processor executable code stored on a memory and executed by at least one processor. The method includes modeling a vector velocity field that measures and quantifies a velocity of electrocardiogram data signals that pass through a local activation time. The method further includes determining codes for each point in plane to provide a color code vector field image; detecting focal and rotor indications by using kernels to scan the color coded vector field image; and classifying the focal and rotor indications into perpetuators.