Ventricular Tachycardia

Ventricular tachycardia (VT) is a rapid, irregular heartbeat originating in the ventricles, posing a significant risk of sudden cardiac death. Current research focuses on developing non-invasive methods for VT localization and classification using deep learning architectures, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), often incorporating attention mechanisms to improve accuracy and interpretability. These advancements aim to improve the speed and accuracy of diagnosis and treatment planning, particularly for catheter ablation procedures, ultimately leading to better patient outcomes. The development of robust machine learning models for VT detection and prediction from ECG signals is a major area of focus, with promising results demonstrated using various algorithms including decision trees and logistic regression.

Papers

January 28, 2023

Localizing the Origin of Idiopathic Ventricular Arrhythmia from ECG Using an Attention-Based Recurrent Convolutional Neural Network
Mohammadreza Shahsavari, Niloufar Delfan, Mohamad Forouzanfar
Visual Creation Single Lead Electrocardiogram Data Ventricular Tachycardia Catheter Ablation

September 16, 2022

Non-invasive Localization of the Ventricular Excitation Origin Without Patient-specific Geometries Using Deep Learning
Nicolas Pilia, Steffen Schuler, Maike Rees, Gerald Moik, Danila Potyagaylo, Olaf Dössel, Axel Loewe
Deep Learning Convolutional Neural Network Patient Specific Fully Connected Layer Electrocardiogram Image Ventricular Tachycardia Catheter Ablation

June 7, 2022

Improved Cardiac Arrhythmia Prediction Based on Heart Rate Variability Analysis
Ashkan Parsi
Heart Rate Variability Cardiac Arrhythmia Ventricular Tachycardia

June 2, 2022

A Deep Learning Network for the Classification of Intracardiac Electrograms in Atrial Tachycardia
Zerui Chen, Sonia Xhyn Teo, Andrie Ochtman, Shier Nee Saw, Nicholas Cheng, Eric Tien Siang Lim, Murphy Lyu, Hwee Kuan Lee
Classification Code Long Short Term Memory Deep Learning Network Activation Map Ventricular Tachycardia

February 25, 2022

MetaVA: Curriculum Meta-learning and Pre-fine-tuning of Deep Neural Networks for Detecting Ventricular Arrhythmias based on ECGs
Wenrui Zhang, Shijia Geng, Zhaoji Fu, Linlin Zheng, Chenyang Jiang, Shenda Hong
Deep Neural Network Machine Learning Model Fine Tuning Curriculum Learning Electrocardiogram Recording Arrhythmia Detection ECG Datasets Ventricular Tachycardia

December 28, 2021

Mythological Medical Machine Learning: Boosting the Performance of a Deep Learning Medical Data Classifier Using Realistic Physiological Models
Ismail Sadiq, Erick A. Perez-Alday, Amit J. Shah, Ali Bahrami Rad, Reza Sameni, Gari D. Clifford
System Performance AI Model Synthetic Electrocardiogram Ventricular Tachycardia

December 24, 2021

Ventricular Tachycardia

Papers

Localizing the Origin of Idiopathic Ventricular Arrhythmia from ECG Using an Attention-Based Recurrent Convolutional Neural Network

Non-invasive Localization of the Ventricular Excitation Origin Without Patient-specific Geometries Using Deep Learning

Improved Cardiac Arrhythmia Prediction Based on Heart Rate Variability Analysis

A Deep Learning Network for the Classification of Intracardiac Electrograms in Atrial Tachycardia

MetaVA: Curriculum Meta-learning and Pre-fine-tuning of Deep Neural Networks for Detecting Ventricular Arrhythmias based on ECGs

Mythological Medical Machine Learning: Boosting the Performance of a Deep Learning Medical Data Classifier Using Realistic Physiological Models

Machine Learning-based Efficient Ventricular Tachycardia Detection Model of ECG Signal

Supraventricular Tachycardia Detection and Classification Model of ECG signal Using Machine Learning