Cardiac EP Map Quality Scoring for Automated Data Point Selection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional EP mapping systems face challenges in efficiently filtering and selecting high-quality data points from vast amounts of intra-cardiac electrophysiological signals, leading to suboptimal map quality and increased physician workload due to manual threshold adjustments.
Innovation Solution
The implementation of an automated data point scoring algorithm (SMARTMAP) that prioritizes high-quality data points for EP map generation, using a smart index based on parameters like cycle length, signal amplitude, and electrode contact, allowing real-time data point replacement and visualization of map quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated processing algorithms are used to filter and select data points, then physician workload is reduced and processing speed is improved, but system complexity and algorithm parameter tuning difficulty increase
Solution Approach 1:
The system automatically adjusts filtering thresholds and selects data points based on quality metrics without requiring continuous manual intervention. The algorithm self-tunes by evaluating signal characteristics and automatically replacing suboptimal data points, enabling the system to serve itself in optimizing map quality.
Solution Approach 2:
The system dynamically adjusts filtering parameters and selection criteria based on real-time analysis of signal quality, noise levels, and data point characteristics. By changing parameters adaptively rather than using fixed thresholds, the system maintains high productivity while managing complexity through intelligent parameter management.
2Manufacturing precision
If multiple filtering criteria are applied to ensure data quality, then map accuracy is improved, but the number of rejected data points increases and processing efficiency decreases
Solution Approach 1:
The system applies filtering criteria selectively rather than uniformly to all data points. By using quality metrics to identify and prioritize high-value data points for replacement, the system achieves high map quality without rejecting excessive data, thereby maintaining processing efficiency while ensuring accuracy.
Solution Approach 2:
The system continuously monitors data point quality metrics and uses this feedback to adjust selection and replacement decisions. By incorporating feedback loops that evaluate the impact of data point inclusion on overall map quality, the system optimizes the balance between accuracy and processing efficiency.
3Measurement precision
If manual threshold adjustments are used to optimize data selection, then data point quality is improved, but physician workload and time consumption increase
Solution Approach 1:
The system automatically performs threshold optimization and data point selection without requiring manual physician intervention. The algorithm independently evaluates data quality metrics and adjusts selection criteria to achieve optimal map quality, freeing physicians from time-consuming manual adjustments.
Solution Approach 2:
The system replaces manual mechanical adjustment processes with automated computational algorithms. By substituting physician manual threshold tuning with computer-based automated selection and replacement algorithms, the system maintains high selection accuracy while eliminating time loss associated with manual operations.
4Reliability
If continuous monitoring and replacement of data points is implemented, then EP map quality is maintained, but computational load and processing time increase
Solution Approach 1:
The system implements periodic evaluation and selective replacement of data points rather than continuous monitoring of all points. By using quality metrics to identify candidate points for replacement and applying updates at optimized intervals, the system maintains map quality consistency while reducing unnecessary computational load.
Data Source
Figure 1
Figure 2
Figure 3~4
AI summary
A system for generating an electrophysiological (EP) map includes a display and a processor. The processor is configured to (i) receive multiple EP data points comprising respective locations and EP values, generated from signals acquired by one or more electrodes of a catheter that are in contact with tissue of a cardiac chamber, (ii) score the received data points with respective quality scores, (iii) for a given unit volume of the EP map, select, from among the data points whose locations fall in the unit volume, a data point with a highest quality score, for use in generating the EP map, and (iv) visualize the EP map to a user, on the display.