Electrophysiological Descriptor Selection for Cardiac Signal Discrimination
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for analyzing cardiac electrical signals using surface electrodes focus on a limited number of indicators, providing an insufficient view of cardiac activity and failing to detect characteristic electrophysiological activities, particularly when they are at a low level or masked by larger patterns.
Innovation Solution
A method for selecting a subset of electrophysiological descriptors that includes estimating values for predefined descriptors in patient populations with and without a predefined condition, generating characteristic vectors, and applying proximity factors to iteratively select a non-redundant subset of descriptors that discriminate between these populations, considering various signal types, markers, and statistical modalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a limited number of cardiac activity indicators are used, then the analysis is simple and focused, but the view of cardiac activity is insufficient and characteristic electrophysiological activities are not detected
Solution Approach 1:
The patent segments the comprehensive set of electrophysiological descriptors into multiple subsets through iterative selection. Each subset contains descriptors that are statistically independent and contribute uniquely to characterizing cardiac activity, thereby maintaining information completeness while managing complexity through structured division
Solution Approach 2:
The patent transforms the selection problem from a one-dimensional choice of individual descriptors to a multi-dimensional optimization involving statistical independence, discriminative power, and information redundancy. This is achieved by evaluating descriptors in a multidimensional space that considers their relationships with multiple other descriptors simultaneously
2Reliability
If multiple electrophysiological descriptors are selected to characterize cardiac activity, then the discrimination between patient populations improves, but the complexity of analysis increases
Solution Approach 1:
The patent applies partial action by selecting only the necessary number of descriptors that provide sufficient discrimination between patient populations. Rather than using all available descriptors, the method iteratively selects subsets until the desired discrimination accuracy is achieved, avoiding unnecessary complexity
Solution Approach 2:
The patent performs preliminary statistical analysis to evaluate the independence and discriminative power of descriptors before final selection. This preliminary evaluation allows the method to pre-identify promising descriptors and eliminate redundant ones early in the process, reducing subsequent analysis complexity
3Ease of operation
If a small number of parameters are focused on, then the measurement is simple, but characteristic electrophysiological activities at low level or masked by larger patterns are not detected
Solution Approach 1:
The patent creates a universal selection framework that evaluates descriptors based on multiple criteria simultaneously: statistical independence, discriminative power, and ability to detect subtle electrophysiological activities. This multi-functional evaluation approach allows the same set of selected descriptors to serve multiple diagnostic purposes without requiring separate measurement protocols
Data Source
AI summary
A method for selecting a subset of electrophysiological descriptors from a set of electrophysiological descriptors, includes procedures for estimating values of a set of electrophysiological descriptors and selecting a subset of descriptors as a function notably of quantifications of proximity factors.