Validation of a simple model for the morphology of the T wave in unipolar electrograms

Abstract

Local unipolar electrograms (UEGs) permit assessment of local activation and repolarization times at multiple sites simultaneously. However, UEG-based indexes of local repolarization are still debated, in particular for positive T waves. Previous experimental and computer modeling studies have not been able to terminate the debate. In this study we validate a simple theoretical model of the UEG and use it to explain how repolarization statistics in the UEG relate to those in the action potential. The model reconstructs the UEG by taking the difference between an inverted local action potential and a position-independent remote signal. In normal tissue, this extremely simple model predicts T-wave morphology with surprising accuracy while explaining in a readily understandable way why the instant of repolarization is always related to the steepest upstroke of the UEG, both in positive and negative T waves, and why positive T waves are related to early repolarizing sites, whereas negative T waves are related to late repolarizing sites.

Coupling of ventricular action potential duration and local strain patterns during reverse remodeling in responders and nonresponders to cardiac resynchronization therapy

“…30 Unlike other measures of repolarization ARI has the advantage of providing local information in contrast to the QT interval and avoids the proarrhythmic risk of premature stimulation during estimation of refractory periods.…”

Using Populations of Models to Navigate Big Data in Electrophysiology: Evaluation of Parameter Sensitivity of Action Potential Models

“…Measurements were made at three representative times of the experiment: 90 min, 120 min and 150 min after the start of the working mode. The ARI is a surrogate measurement for action potential duration at 90% repolarization (APD90) [11], but it provides no further information on action potential shape; different action potential morphologies can have the same APD90, which means that two cells exhibiting the same duration can be under different physiological conditions, this is called inter-subject variability.…”

Evaluating the Risks of Arrhythmia through Big Data: Automatic Processing and Neural Networks to Classify Epicardial Electrograms

“…Special consideration has to be given to the T wave since, as shown by Potse et al [5], it can be either positive, negative or biphasic. A T-wave peak occurring before the recovery time will always be negative, whereas a peak after the recovery time will always be positive, regradless of the morphology of the T wave.…”

“…Previous studies have shown how pathological behaviors at a cellular level may lead to dangerous arrhythmic behaviors [1] and how these are reflected in the epicardial electrogram [2][3][4][5][6]. Authors have pointed out the importance and possible impact of performing automatic analyses of electrograms [7]; but surprisingly, there is, nowadays, a gap in the literature involving the automatic extraction of features from electrograms and their evaluation is usually done visually and manually.…”