Electrocardiographic Mapping for Automated Cardiac Target Identification
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Solution Overview
Problem
Existing electrocardiographic mapping methods for identifying treatment sites for cardiac arrhythmias rely heavily on clinician judgment, leading to variability in ablation efficacy.
Innovation Solution
A system and method for automatically identifying target sites on a patient's heart using signal segmentation, reconstruction of electrophysiological signals based on geometry data, and generating maps to pinpoint precise treatment locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated signal segmentation and reconstruction algorithms are implemented, then measurement precision and target site identification accuracy are improved, but device complexity increases
Solution Approach 1:
The system segments the complex task of target site identification into distinct functional modules: signal acquisition from ECG data, morphology evaluation by the signal segment evaluator, reconstruction of electrophysiological signals by the reconstruction engine, map generation by the map generator, and target site identification by the target generator. This modular segmentation improves measurement precision at each stage while making the overall device complexity manageable through structured organization.
Solution Approach 2:
The patent introduces intermediate processing layers including the signal segment evaluator that assesses signal morphology, the reconstruction engine that creates electrophysiological signal maps, and the map generator that visualizes data. These intermediaries transform raw ECG signals into progressively more informative representations, improving target site identification accuracy while distributing computational complexity across multiple specialized components rather than one monolithic system.
2Productivity
If automated algorithms are used for target site identification, then productivity and efficiency are improved, but ease of operation decreases due to reduced clinician control
Solution Approach 1:
The system implements self-service capabilities where the automated algorithms independently perform signal segmentation, morphology evaluation, reconstruction, and target site identification without requiring continuous clinician intervention. The signal segment evaluator automatically identifies relevant signal segments, the reconstruction engine autonomously reconstructs electrophysiological signals, and the target generator independently identifies target sites, thereby improving productivity while maintaining operational simplicity.
Solution Approach 2:
The system incorporates feedback mechanisms where the automated algorithms process clinical data and generate target site recommendations that are presented to clinicians for review and confirmation. This feedback loop maintains ease of operation by allowing clinician oversight and control while still providing the productivity benefits of automated processing, as clinicians can efficiently review and approve algorithm-generated recommendations without manual analysis of raw signals.
Data Source
AI summary
In an example, a signal segment evaluator can be programmed to evaluate a morphology of at least one electrophysiological signal to identify a signal segment of interest. The morphology of the signal segment of interest can be indicative of an electrophysiological event of a patient during a respective time interval. A reconstruction engine can be programmed to reconstruct electrophysiological signals on a surface of interest within a body of the patient based on the electrophysiological signals measured from an outer surface of the patient and geometry data representing an anatomy of the patient. A map generator can be programmed to generate a map representing the reconstructed electrophysiological signals on the surface of interest for the respective time interval of the signal segment of interest. A target generator can be programmed to identify a target site within the patient's body based on the map for the electrophysiological event.