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High-Throughput Analysis of Optical Mapping Data Using ElectroMap
Published on: June 4, 2019
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In silico pace mapping identifies pacing sites more accurately than inverse body surface potential mapping.
Fernando O Campos1, Nadeev Wijesuriya2, Mark K Elliott2
1School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
Heart Rhythm
|December 30, 2024
Summary
In silico pace mapping shows superior accuracy to electrocardiographic imaging (ECGi) for identifying ventricular pacing sites. This computational approach offers improved precision for ablation planning in tachycardia patients.
Area of Science:
- Cardiovascular Imaging
- Computational Modeling
- Electrophysiology
Background:
- Electrocardiographic imaging (ECGi) reconstructs epicardial activation but has limitations.
- In silico pace mapping creates virtual 3D pace maps using computational models and ECGs.
Purpose of the Study:
- To compare the accuracy of ECGi and in silico pace mapping in determining ventricular pacing sites.
Main Methods:
- Personalized CT-based ventricular models were created and aligned with ECGi electrodes.
- Virtual pacing at 1000 random sites generated simulated ECGs and body surface potentials (BSPs).
- In silico pace maps were reconstructed by correlating simulated and clinical signals; accuracy was measured by distance to the pacing origin.
Main Results:
- In silico pace mapping significantly outperformed ECGi in locating pacing origins.
- Spatial accuracy for in silico pace mapping was 9.5 mm (BSPs) and 12.2 mm (ECGs) for LV pacing, versus 30.8 mm for ECGi.
- For RV pacing, distances were 26.1 mm (BSPs), 30.9 mm (ECGs), and 29.1 mm (ECGi).
Conclusions:
- In silico pace mapping demonstrates higher accuracy than ECGi for detecting paced activation.
- Optimal performance was achieved using all BSPs, with reduced accuracy during RV apical pacing.
