Cardiac Activity Mapping via Wavelet Scalogram Analysis
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Solution Overview
Problem
In cardiac diagnostic and therapeutic procedures, existing technologies face challenges in detecting and decoupling multi-component cardiac signals, particularly in areas with scar or wall thinning, where sharp fractionated bi-polar potentials are often fused with far-field electrograms, making it difficult to distinguish near-field and far-field activities.
Innovation Solution
A method involving the transformation of electrogram signals into the wavelet domain using continuous wavelet transformation to compute scalograms, energy functions, and metrics such as QRS activity duration, near-field and far-field component durations, and energy ratios, allowing for the separation and graphical representation of near-field and far-field components on a cardiac model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional electrogram recording methods are used, then the recording process is simple, but near-field and far-field potentials become fused and cannot be distinguished
Solution Approach 1:
The patent applies segmentation by dividing the fused electrogram signal into distinct near-field and far-field components through wavelet transformation. The continuous wavelet transform decomposes the signal into different frequency bands and time segments, allowing separate identification and analysis of near-field potentials (higher frequency components) and far-field potentials (lower frequency components) that are otherwise fused in conventional recordings.
Solution Approach 2:
The patent transitions from conventional time-domain analysis to a time-frequency domain representation using wavelet scalograms. This dimensional change from one-dimensional time signals to two-dimensional time-frequency maps enables simultaneous visualization of frequency content evolution over time, providing an additional dimension for distinguishing between near-field and far-field activities based on their characteristic frequency signatures.
2Measurement precision
If signal filtering methods are used to separate components, then component separation is achieved, but signal distortion occurs
Solution Approach 1:
The patent employs parameter changes by dynamically adjusting wavelet decomposition levels and frequency band thresholds based on the specific characteristics of each electrogram signal. Rather than applying fixed filtering parameters, the system adapts the wavelet transform parameters (such as decomposition depth and frequency cutoffs) to match the actual signal properties, enabling effective component separation while preserving the authentic morphology and timing of cardiac potentials.
3Measurement precision
If multiple signal processing steps are applied to decouple components, then near-field and far-field activities are distinguished, but processing time increases
Solution Approach 1:
The patent applies preliminary action by performing wavelet decomposition and energy function calculation in advance to generate pre-processed feature maps before final component identification. The continuous wavelet transform and energy function computations are executed as preliminary steps that create enhanced feature representations, allowing subsequent near-field and far-field detection to proceed more efficiently with reduced computational burden during critical analysis phases.
Data Source
AI summary
Cardiac activity can be mapped by receiving an electrogram, transforming the electrogram into the wavelet domain (e.g., using a continuous wavelet transformation) to create a scalogram of the electrogram, computing at least one energy function of the scalogram, and computing at least one metric of the electrogram using the at least one energy function. The metrics of the electrogram can include, without limitation: a QRS activity duration for the electrogram; a near-field component duration for the electrogram; a far-field component duration for the electrogram; a number of multiple components for the electrogram; a slope of a sharpest component of the electrogram; a scalogram width; an energy ratio in the electrogram; and a cycle-length based metric of the electrogram.