ECG Heart Vector Curvature Analysis for Ventricular Tachycardia Risk
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Solution Overview
Problem
Current methods for analyzing electrocardiograms (ECGs) to predict ventricular tachycardia risk are limited by the lack of reliable operator-independent end-points for VT ablation, leading to inconsistent results and high recurrence rates after VT ablation.
Innovation Solution
A system and method that analyze ECG signals to identify a heart vector, measure its velocity and change in curvature, and determine the risk of ventricular tachycardia, using filtered ECG signals and a processor to quantify these parameters for improved VT ablation techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the inducibility end-point is used for VT ablation, then the procedure can be standardized, but the measurement precision and reliability are reduced due to operator dependence and procedural limitations
Solution Approach 1:
The patent replaces manual operator assessment of VT inducibility with an automated computational analysis system that processes ECG signals. The system uses algorithms to detect inapparent conduction abnormalities and calculate VT risk scores, substituting the mechanical/manual process of electrophysiological studies with an automated digital system that eliminates operator dependence while maintaining standardization.
Solution Approach 2:
The patent introduces an intermediary computational layer between the raw ECG signals and the VT risk assessment. This intermediary system filters, analyzes, and quantifies conduction abnormalities automatically, serving as a mediator that translates complex electrical signals into reliable risk scores without requiring direct operator interpretation or invasive electrophysiological studies.
2Productivity
If VT ablation is performed based on current end-points, then the procedure can be completed, but the manufacturing precision (ablation accuracy) is reduced leading to high recurrence rates
Solution Approach 1:
The patent performs preliminary analysis of ECG signals to identify inapparent conduction abnormalities and precisely localize damaged heart tissue before the ablation procedure. By pre-mapping the exact locations of conduction defects using automated algorithmic analysis, the system enables surgeons to target ablation precisely, improving manufacturing precision while maintaining procedural efficiency.
Solution Approach 2:
The patent replaces the traditional mechanical approach of invasive electrophysiological mapping with automated computational analysis of non-invasive ECG signals. This substitution provides more precise localization of damaged tissue without the limitations of operator-dependent procedures, thereby improving ablation accuracy and reducing recurrence rates.
3Measurement precision
If invasive electrophysiological studies are performed to assess VT risk, then the measurement precision improves, but the device complexity and procedural risk increase
Solution Approach 1:
The patent substitutes invasive mechanical electrophysiological studies with automated computational analysis of non-invasive ECG signals. The system uses algorithms to detect subtle conduction abnormalities that were previously only visible through invasive procedures, achieving comparable or superior measurement precision while eliminating the complexity and risks of invasive catheter-based studies.
Solution Approach 2:
The patent creates a computational model that replicates the diagnostic value of invasive electrophysiological studies using non-invasive ECG data. By copying the essential information about conduction abnormalities from surface ECGs through algorithmic analysis, the system achieves accurate VT risk assessment without requiring invasive procedures, thereby reducing device complexity and procedural risk.
Data Source
AI summary
A method and system for determining a patient's risk of ventricular tachycardia are disclosed. The method includes receiving ECG signals from a patient and filtering the collected ECG signals to generate filtered ECG signals. The method further includes identifying a heart vector from the filtered ECG signals, and measuring a velocity of the heart vector movement. A change in curvature of the identified heart vector movement is quantified and a risk of ventricular tachycardia is determined based at least on the measured velocity and the quantified change in curvature of the identified heart vector movement.


