Feature State Change Detection for Real-Time Cardiac Ablation
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Solution Overview
Problem
Existing techniques are unable to accurately quantify arrhythmogenic activity states, detect changes in electrical activity during cardiac ablation procedures, and provide real-time feedback to clinicians, leading to inefficiencies in identifying and treating arrhythmia drivers.
Innovation Solution
A system and method for detecting feature state changes in electrophysiological signals using a feature detector that computes feature signals and states, identifies new arrhythmia drivers, and provides real-time treatment suggestions based on detected changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing techniques are used to monitor electrical activity during cardiac ablation, then basic electrical signals can be captured, but accurate quantification of arrhythmogenic activity states and detection of changes cannot be achieved
Solution Approach 1:
The patent segments the electrophysiological signal into multiple features (e.g., conduction velocity, activation time, refractory period) and analyzes each feature's state changes independently. This segmentation allows precise quantification of arrhythmogenic activity by breaking down the complex signal into manageable components that can be individually measured and compared against baseline values.
Solution Approach 2:
The system monitors changes in specific electrophysiological parameters (conduction velocity, activation time, refractory period) and detects state changes when these parameters exceed predefined thresholds. By tracking parameter changes rather than relying on raw signal morphology, the system achieves accurate quantification of arrhythmogenic activity states during ablation procedures.
2Productivity
If real-time detection of electrical activity changes is implemented, then treatment efficacy can be improved, but processing time and computational resources increase
Solution Approach 1:
The system pre-establishes baseline values for electrophysiological features and predefined thresholds for state changes before the ablation procedure begins. During the procedure, the system only needs to compare current measurements against these pre-set criteria rather than performing complex real-time analysis, significantly reducing processing time while maintaining real-time feedback capability.
Solution Approach 2:
The patent extracts specific meaningful features from the raw electrophysiological signal (such as conduction velocity, activation time, refractory period) and focuses analysis only on these extracted features rather than processing the entire signal. This extraction approach reduces computational burden and processing time while preserving the essential information needed for real-time detection of arrhythmogenic changes.
3Measurement precision
If comprehensive monitoring of electrical activity is performed, then detection accuracy improves, but the ability to identify and treat arrhythmia drivers decreases due to inefficiency
Solution Approach 1:
The system provides real-time feedback by continuously monitoring electrophysiological features and immediately detecting when state changes occur that indicate arrhythmogenic activity. When a feature change is detected, the system can alert operators and potentially trigger automated responses, enabling timely identification and treatment of arrhythmia drivers without requiring comprehensive manual analysis of all signal data.
Data Source
AI summary
Examples are described herein for feature state change detection and used thereof. In some examples, feature signals can be generated based on electrophysiological data captured from a patient. The feature signals can be evaluated to compute feature states. A feature state change can be detected indicative of a change in electrical activity caused at a location on a surface of interest within a patient's body. In some examples, the feature state change is used to identify a potential target site for a therapy. In other examples, the feature state change can be used for treatment suggestion and success recommendations and thus driving a treatment being applied to the patient. Other examples and uses of feature states and/or detected feature state changes are disclosed herein.