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.
The kinematic and morphological abnormalities can be used for accurate detection of myocardial infarction without contrast agents. It has important implications for the early treatment of patients. However, methods based on motion tracking are time-consuming, and the complex movements of the heart make them difficult to implement. In this paper, we propose a deep learning constrained framework based on relative motion features. It can detect myocardial infarction areas through cine cardiac magnetic resonance imaging(CMRI) images. It includes one relative motion extraction component and one deep neural network component. In the relative motion model, a U-Net model is used to segment the myocardial contour. After that, the motion features and pixel features of the myocardium are extracted and fused. Finally, the extracted relative features are further learned via the deep neural network model based on ConvLSTM to predict the myocardial infarction area. Our method doesn’t need a pre-find position match and is more suitable for the physiological process of the myocardium. We validated the performance of our framework in 276 cine CMRI sequences datasets, and it yielded a high consistency with manual delineation (90.8% detection accuracy). The results demonstrate that our proposed method can be an attractive tool for the diagnosis of myocardial infarction in the clinic.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.