Key result
DenseNet-based neural network automatically detects abnormal ventricular potential onsets with ~26 ms error.
Why the study?
Identifying abnormal ventricular potentials helps localize arrhythmogenic myocardial areas sustaining ventricular tachycardia, prompting the exploration of a novel approach to delineate their pathological deflection onsets.
Does a DenseNet-based convolutional neural network accurately delineate the onset of abnormal ventricular potentials in electrograms from patients with post-ischemic VT?
Does a DenseNet-based convolutional neural network accurately delineate the onset of abnormal ventricular potentials in electrograms from patients with post-ischemic VT?
A DenseNet-based convolutional neural network demonstrates feasibility in automatically detecting the onset of abnormal ventricular potentials, which may support the estimation of local conduction velocity in arrhythmogenic regions.
Abnormal ventricular potentials (AVPs) are fractionated and complex electrograms (EGMs), typically associated with slow conduction areas in the myocardium. As such, in ventricular tachycardia (VT), their identification supports the localization of the myocardial areas sustaining the arrhythmia in the electroanatomic mapping. Their latency can be related to their spatial localization, suggesting the entrance or the exit of a reentry pathway. In this work, we explored a novel approach to delineate the onset of the pathological deflections in AVPs. For this preliminary study, an expert cardiologist was asked to mark the onset of such pathological deflections in the identified AVPs in EGMs obtained from five patients affected by post-ischemic VT. On this basis, we trained a DenseNet-based convolutional neural network to automatically detect the AVP onsets, by using a time-frequency representation of the signal. The results of the model predictions have been compared to the ground truth in a regression analysis, yielding a root mean square error of 26 ms and a correlation coefficient of 0.6 on average. Notably, the identification of both onset and duration of abnormalities in EGMs may support the process of estimation of local conduction velocity and direction in arrhythmogenic regions.
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Pitzus et al. (2024) studied Post-ischemic ventricular tachycardia (n=5). DenseNet-based convolutional neural network vs. Expert cardiologist annotations (ground truth) was evaluated on Model prediction accuracy (root mean square error and correlation coefficient) (RMSE 26 ms, r=0.6). A DenseNet-based convolutional neural network automatically detected abnormal ventricular potential onsets with a root mean square error of 26 ms and a correlation coefficient of 0.6.
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