Novel hybrid method for interpolating missing information in body surface potential maps.

Area of Science:

• Electrophysiology
• Medical Imaging
• Data Science

Background:

• Body surface potential mapping (BSPM) offers crucial electrophysiological data for cardiac disease detection.
• BSPM is integrated into clinical electrocardiographic imaging (ECGI) systems.
• Data loss in BSPM due to noise or poor electrode contact is a common challenge.

Purpose of the Study:

• To develop and evaluate a novel interpolation method for reconstructing missing data in BSPM.
• The method combines Laplacian minimization and principal component analysis (PCA).
• To assess the performance of the hybrid method against traditional Laplacian and PCA interpolation techniques.

Main Methods:

• A dataset of 117-lead BSPMs from 744 subjects (normal, myocardial infarction, left ventricular hypertrophy) was used.
• Missing data was simulated in 7 regions, including high potential gradient and clinically relevant patterns.
• The hybrid method involved initial Laplacian interpolation followed by PCA-based refinement using the first 15 principal components.

Main Results:

• The hybrid Laplacian-PCA method demonstrated superior performance in interpolating missing BSPM data compared to standalone Laplacian or PCA methods.
• The hybrid method showed significantly lower relative error and higher correlation coefficients across all tested scenarios.
• PCA-based interpolation performed poorly, especially with a high number of missing electrodes.

Conclusions:

• The hybrid Laplacian-PCA interpolation method is highly effective for reconstructing missing BSPM data.
• This technique offers a robust solution for data gaps in BSPM, regardless of the extent or location of missing information.
• The findings support the enhanced utility of BSPM in clinical ECGI for improved cardiac diagnostics.