N-Dimensional Signal Vector Analysis for Cardiac Mapping
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Solution Overview
Problem
In cardiac mapping systems, it is challenging to distinguish between local activation signals and far-field activity, especially in complex heart rhythm disorders where signals from multiple deflections make it difficult to identify local versus far-field activation or noise.
Innovation Solution
A method and system that reconstruct electrical activity propagation by constructing an N-dimensional signal vector using signals from neighboring channels, comparing changes over time to a threshold, and adjusting for latency and scaling differences to discriminate between local and far-field activity, employing an extended bipolar configuration to enhance discrimination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional signal processing methods are used to detect cardiac activation, then all electrical signals are captured including far-field activity, but it becomes difficult to distinguish local activation from far-field activity and noise in complex rhythm disorders
Solution Approach 1:
The patent segments the signal processing by creating an N-dimensional signal vector that separates local activation detection from far-field activity. By constructing a vector from signals on the channel of interest and N neighboring channels, the system segments the detection task into local (channel of interest) and far-field (neighboring channels) components, enabling precise identification of local activation deflections.
Solution Approach 2:
The patent transforms the traditional one-dimensional signal analysis into an N-dimensional signal vector space. By adding spatial dimensions from neighboring channels to the temporal signal dimension, the system creates a multi-dimensional detection framework where local activation can be distinguished from far-field activity through vector magnitude and direction analysis.
2Quantity of substance
If multiple deflections are detected in complex rhythm disorders, then more signal information is available, but it becomes impossible to identify which deflections represent local activation versus far-field activity or noise
Solution Approach 1:
The patent applies local quality by making the detection sensitivity channel-specific. The channel of interest is given special weight in the N-dimensional vector analysis, with local activation detected as significant changes in the vector magnitude primarily driven by that channel. This allows precise identification of which deflections represent local activation at the specific sensor location versus far-field activity.
3Reliability
If far-field signals are included in the analysis, then more comprehensive cardiac activity is captured, but local activation becomes harder to identify due to signal contamination
Solution Approach 1:
The N-dimensional signal vector acts as an intermediary that mediates between far-field signals and local activation detection. By transforming individual channel signals into a vector representation where local activation manifests as significant vector changes, the system uses the vector as an intermediate computational structure that preserves local activation information while filtering out far-field contamination.
Data Source
AI summary
Electrical activity propagation along an electrode array within a cardiac chamber is reconstructed. Signals are sampled from the electrode array including signals from a channel of interest. An N-dimensional signal vector is then constructed using signals from N neighboring channels referenced to the channel of interest. A change in the N-dimensional signal vector over time is then determined and compared to a predetermined threshold to establish whether local activation has occurred on the channel of interest.