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Related Experiment Video

Updated: Jul 18, 2026

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
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A DenseNet-based Abnormal Ventricular Potentials Onset Delineation: A Feasibility Study.

Andrea Pitzus, Christian Cossu, Giulia Baldazzi

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    Summary

    This study introduces a new AI method to pinpoint abnormal ventricular potential onsets in electrograms for better ventricular tachycardia localization. The approach uses a neural network to analyze complex heart signals, aiding in arrhythmia pathway identification.

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    Area of Science:

    • Cardiology
    • Biomedical Engineering
    • Artificial Intelligence

    Background:

    • Abnormal ventricular potentials (AVPs) are complex electrograms indicating slow conduction in the myocardium.
    • Identifying AVPs is crucial for localizing arrhythmogenic areas during ventricular tachycardia (VT) using electroanatomic mapping.
    • The latency of AVPs can provide insights into reentry pathway entrance or exit points.

    Purpose of the Study:

    • To develop and evaluate a novel approach for automatically delineating the onset of pathological deflections in AVPs.
    • To leverage deep learning for precise identification of AVP onsets in electrograms (EGMs).

    Main Methods:

    • A DenseNet-based convolutional neural network was trained to detect AVP onsets.
    • The model utilized time-frequency representations of EGMs from five post-ischemic VT patients.
    • An expert cardiologist provided ground truth annotations for AVP onsets.

    Main Results:

    • The AI model achieved an average root mean square error of 26 ms in predicting AVP onsets.
    • A correlation coefficient of 0.6 was observed between model predictions and expert annotations.
    • The study demonstrated the feasibility of automated AVP onset detection.

    Conclusions:

    • Automated detection of AVP onsets can support the localization of myocardial areas sustaining VT.
    • Identifying both the onset and duration of EGMs abnormalities may aid in estimating local conduction velocity and direction.
    • This AI-driven approach shows promise for enhancing electrophysiological mapping and arrhythmia management.