High-Resolution Measurement of Local Activation Time Differences From Bipolar Electrogram Amplitude

Abstract

Localized changes in myocardial conduction velocity (CV) are pro-arrhythmic, but high-resolution mapping of local CV is not yet possible during clinical electrophysiology procedures. This is in part because measurement of local CV at small spatial scales (1 mm) requires accurate annotation of local activation time (LAT) differences with very high temporal resolution (≤1 ms), beyond that of standard clinical methods. We sought to develop a method for high-resolution measurement of LAT differences and validate against existing techniques. First, we use a simplified theoretical model to identify a quantitative relationship between the LAT difference of a pair of electrodes and the peak amplitude of the bipolar EGM measured between them. This allows LAT differences to be calculated from bipolar EGM peak amplitude, by a novel “Determination of EGM Latencies by Transformation of Amplitude” (DELTA) method. Next, we use simulated EGMs from a computational model to validate this method. With 1 kHz sampling, LAT differences less than 4 ms were more accurately measured with DELTA than by standard LAT annotation (mean error 3.8% vs. 22.9%). In a 1-dimensional and a 2-dimension model, CV calculations were more accurate using LAT differences found by the DELTA method than by standard LAT annotation (by unipolar dV/dt timing). DELTA-derived LAT differences were more accurate than standard LAT annotation in simulated complex fractionated EGMs from a model incorporating fibrosis. Finally, we validated the DELTA method in vivo using 18,740 bipolar EGMs recorded from the left atrium of 10 atrial fibrillation patients undergoing catheter ablation. Using clinical EGMs, there was agreement in LAT differences found by DELTA, standard LAT annotation, and unipolar waveform cross-correlation. These results demonstrate an underlying relationship between a bipolar EGM’s peak amplitude and the activation time difference between its two electrodes. Our computational modeling and clinical results suggest this relationship can be leveraged clinically to improve measurement accuracy for small LAT differences, which may improve CV measurement at small spatial scales.

A Review of Personalised Cardiac Computational Modelling Using Electroanatomical Mapping Data

“…These include probabilistic, hybrid and inverse methods that aim to mitigate the primary sources of error encountered when annotating LATs: far-field signal artefacts in unipolar EGMs, orientation ambiguity in the case of bipolar EGMs and uncertainty associated with acquiring intracardiac measurements. [38][39][40][41][42] Table 1 lists techniques organised by category, along with the expected advantages that these approaches can provide towards model personalisation.…”

Impact of electrode orientation, myocardial wall thickness, and myofiber direction on intracardiac electrograms: numerical modeling and analytical solutions

“…In recent years, the focus has shifted from studying electrograms analytically to doing numerical studies in order to understand and predict iEGMs. In silico studies were carried out by Blauer et al (2014) and Gaeta et al (2020Gaeta et al ( , 2021 to figure out the effect of directionality and electrode spacing on bipolar amplitudes. Gaeta et al derived a theoretical model based on local activation times (LATs) and validated it with clinical data.…”

An Efficient Hybrid Methodology for Local Activation Waves Detection under Complex Fractionated Atrial Electrograms of Atrial Fibrillation

“…However, bipolar EGMs, which are vastly used in AF mapping, are especially sensitive to various parameters such as the wavefront direction, the electrode specifications and filtering that may affect the amplitude and morphology of the signals [ 27 , 28 , 29 , 30 ]. Despite the development of methods aiming to facilitate the automatic annotation of bipolar EGMs, the aforementioned parameters significantly complicate the LAWs detection in CFAEs and EGMs with a high fractionation degree [ 31 ]. Taking all the afore into account, the development of algorithms able to reliably detect LAWs from CFAEs has been a rather complicated issue.…”