Cardiac Wall Thickness Estimation via Electrical Impedance
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Solution Overview
Problem
Existing methods for estimating cardiac wall thickness, such as ultrasound, fluoroscopy, and MRI, are costly and complex, and their spatial resolution may yield inaccurate results during cardiac ablation procedures, risking undesired tissue damage or harm to adjacent structures.
Innovation Solution
A machine learning model, such as an artificial neural network, uses multi-channel ECG and intra-cardiac electrograms acquired by a catheter to estimate cardiac wall thickness during ablation, trained with ground truth data from imaging modalities like ultrasound, CT, and ablation data like temperature rise and impedance change, enabling real-time assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If imaging modalities like ultrasound, MRI, or fluoroscopy are used to estimate cardiac wall thickness, then measurement capability is provided, but cost increases and device complexity increases
Solution Approach 1:
The patent replaces complex mechanical imaging systems (ultrasound, MRI, fluoroscopy) with an electrical field-based measurement system. Electroporation pulses are applied through electrodes to induce cellular permeabilization, and the resulting electrical impedance changes are measured to estimate tissue properties and wall thickness, substituting mechanical/optical imaging with electrical measurement methods.
Solution Approach 2:
The patent introduces electrical impedance as an intermediary parameter to infer cardiac wall thickness. Instead of directly imaging the tissue structure, the system measures electrical impedance changes during electroporation, which correlate with tissue properties and wall thickness, using impedance as a mediator between the applied electrical field and the structural information sought.
2Measurement precision
If imaging modalities are used to assess cardiac wall thickness, then structural information is obtained, but the spatial resolution may be insufficient leading to inaccurate results
Solution Approach 1:
The patent applies local quality by focusing electrical measurement on the specific local region where electrodes contact the tissue. The electroporation effect and impedance measurement are highly localized to the immediate tissue surrounding the electrodes, providing precise local tissue property information without being constrained by the spatial resolution limits of global imaging modalities.
3Reliability
If traditional imaging methods are used during ablation procedures, then wall thickness information is available, but cost increases
Solution Approach 1:
The patent implements multi-functionality by using the same electrode system for multiple purposes: delivering electroporation pulses for ablation, measuring electrical impedance for tissue characterization, and estimating wall thickness for safety monitoring. This eliminates the need for separate imaging equipment, reducing procedural costs while maintaining ablation safety.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate and cost-effective estimation of cardiac wall thickness during ablation, improving procedural outcomes by optimizing ablation parameters and reducing tissue damage.
Implementation Method 1
A machine learning model, such as an artificial neural network, uses multi-channel ECG and intra-cardiac electrograms acquired by a catheter to estimate cardiac wall thickness during ablation
Implementation Method 2
trained with ground truth data from imaging modalities like ultrasound, CT, and ablation data like temperature rise and impedance change
Implementation Method 3
ablation data like temperature rise and impedance change
Data Source
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AI summary
A system includes an interface and a processor. The interface is configured to receive a plurality of electrophysiological (EP) measurements performed in a heart of a patient. The processor is configured to estimate a wall thickness at a specified location of the heart based on the EP measurements.