Neural Network His Bundle Detection for Precise Cardiac Ablation
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Solution Overview
Problem
Conventional methods for manually tagging the His bundle during cardiac ablation procedures are tedious, time-consuming, and prone to false positives, posing risks to the electrical conduction system of the heart.
Innovation Solution
A system utilizing a first catheter and a processor with a neural network to automatically detect the His bundle by analyzing electrophysiological data and distance data, trained with predetermined electrophysiological data and threshold values, to accurately identify the His bundle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tagging of the His bundle is performed during cardiac ablation procedures, then the location of the His bundle can be identified, but the process becomes tedious, time-consuming, and prone to false positives
Solution Approach 1:
The system enables automated detection of the His bundle using neural networks that process electrophysiological data and distance data from catheters, allowing the system to identify the His bundle location autonomously without requiring manual tagging by physicians, thereby eliminating the time-consuming nature of manual identification while maintaining or improving detection accuracy
Solution Approach 2:
The patent replaces the manual mechanical tagging process with an automated computational system using neural networks that analyze electrophysiological signals and spatial distance data, substituting human manual operation with an automated algorithmic system that processes multiple data streams to identify the His bundle location
2Reliability
If manual tagging of the His bundle is performed during cardiac ablation procedures, then the location of the His bundle can be identified, but false positive reads occur where ECG signals appear to look like His bundle impulses but are not
Solution Approach 1:
The system merges multiple data sources including electrophysiological data from the first catheter, distance data from the second catheter positioned at a predetermined location, and predetermined threshold values into a unified neural network analysis framework, combining multiple independent detection criteria to reduce false positives and improve the reliability of His bundle identification
Solution Approach 2:
The neural network system performs multiple functions simultaneously: it processes electrophysiological signals, analyzes spatial distance data, compares against predetermined thresholds, and integrates these multiple detection modalities into a single comprehensive identification system that reduces false positives while managing complexity through unified processing
3Productivity
If automated detection using neural networks is implemented, then manual tagging time is reduced and detection accuracy is improved, but system complexity increases
Solution Approach 1:
The neural network acts as an intermediary processing layer that receives multiple types of input data (electrophysiological signals, distance measurements, threshold values) and transforms them into a unified detection output, managing the complexity of integrating multiple data sources while enabling rapid automated detection without requiring direct complex interactions between all system components
Data Source
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AI summary
A system for training a neural network to automatically detect a cardiac structure of interest includes a processor comprising a neural network training model that receives training data. The training data comprises a first input comprising first electrophysiological data regarding a first cardiac structure received by an electrode of a first catheter positioned within a heart, and a second input comprising a second data relating to the first cardiac structure. The neural network training model generates, as an output, a determination of whether the first cardiac structure is the cardiac structure of interest based on the training data.