A deep learning method for localizing the origin of ventricular tachycardia using 12-lead ECG

Ao Ran; Chengjin Yu; Huafeng Liu

doi:10.1117/12.2653034

7 April 2023 A deep learning method for localizing the origin of ventricular tachycardia using 12-lead ECG

Ao Ran, Chengjin Yu, Huafeng Liu

Proceedings Volume 12465, Medical Imaging 2023: Computer-Aided Diagnosis; 124651Y (2023) https://doi.org/10.1117/12.2653034
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

Heart disease is a common group of circulatory diseases, but it seriously affects people’s life and health. Among many heart diseases, ventricular tachycardia (VT) is the most common cause of death. The current clinical treatment plan is to treat ventricular tachycardia by finding the origin of ventricular activation and then ablating it with an invasive catheter. However, the longer the catheter remains in the body, the more dangerous it is for the patient, so it is of interest to achieve noninvasive assisted localization of the origin site. To solve this problem, we propose an end-to-end neural network structure, Densefomer (DenseNet-Transformer Network), in a data-driven manner. This simple and effective model can localize origin of ventricular activation using only 12-lead electrocardiogram (ECG). Densefomer introduces Transformer and DenseNet to obtain global and local feature information, respectively. Our model finally achieved a localization error of 10.79 mm and a classification accuracy of 58.16% on real clinical data.

Citation Download Citation

Ao Ran, Chengjin Yu, and Huafeng Liu "A deep learning method for localizing the origin of ventricular tachycardia using 12-lead ECG", Proc. SPIE 12465, Medical Imaging 2023: Computer-Aided Diagnosis, 124651Y (7 April 2023); https://doi.org/10.1117/12.2653034

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