ADAA: A Morphology-Aware Method for Local Activation Time Computation Using Cross Correlation

Accurate estimation of activation times is crucial in electrophysiological studies to assess depolarization wave propagation direction.Rule-based methods, such as the Steepest Deflection (SD) method, have been prevalent, but their lack of robustness is a major limitation, leading to exploring alternative methodologies.The Directional Activation Algorithm (DDA) [1,2] leverages delays between electrogram (EGM) signals.We generalize the DAA framework by utilizing cross-correlation analysis to compute pairwise relative delays between EGMs.Our Adaptive Direction Activation Algorithm (ADAA) integrates morphological characteristics and initial activation time estimates to enhance accuracy and robustness.Our contribution lies in the introduction of a more robust and general model that can be fitted with the same computational cost as DAA.We formulate the optimization problem and derive a closedform solution.Through evaluation on both toy model data and realistic simulations, we demonstrated the superior efficacy of our methodology in estimating activation times compared to existing methods.In specific settings, our approach reduces the mean squared error (MSE) by 50%.