AIMC Topic: Atrial Fibrillation

Clear Filters Showing 201 to 210 of 339 articles

Detection of Atrial Fibrillation from Single Lead ECG Signal Using Multirate Cosine Filter Bank and Deep Neural Network.

Journal of medical systems
Atrial fibrillation (AF) is a cardiac arrhythmia which is characterized based on the irregsular beating of atria, resulting in, the abnormal atrial patterns that are observed in the electrocardiogram (ECG) signal. The early detection of this patholog...

Big Data and Atrial Fibrillation: Current Understanding and New Opportunities.

Journal of cardiovascular translational research
Atrial fibrillation (AF) is the most common arrhythmia with diverse etiology that remarkably relates to high morbidity and mortality. With the advancements in intensive clinical and basic research, the understanding of electrophysiological and pathop...

A morphology based deep learning model for atrial fibrillation detection using single cycle electrocardiographic samples.

International journal of cardiology
BACKGROUND: Deep learning (DL) has shown promising results in improving atrial fibrillation (AF) detection algorithms. However, these models are often criticized because of their "black box" nature.

Automated extraction of left atrial volumes from two-dimensional computer tomography images using a deep learning technique.

International journal of cardiology
BACKGROUND: Precise segmentation of the left atrium (LA) in computed tomography (CT) images constitutes a crucial preparatory step for catheter ablation in atrial fibrillation (AF). We aim to apply deep convolutional neural networks (DCNNs) to automa...

Detection of Atrial Fibrillation Using 1D Convolutional Neural Network.

Sensors (Basel, Switzerland)
The automatic detection of atrial fibrillation (AF) is crucial for its association with the risk of embolic stroke. Most of the existing AF detection methods usually convert 1D time-series electrocardiogram (ECG) signal into 2D spectrogram to train a...

Automatic diagnosis of the 12-lead ECG using a deep neural network.

Nature communications
The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models. Deep Neural Networks (DNNs) are models composed of stacked transformations that learn tasks by examples. This technology has re...

Video-based AI for beat-to-beat assessment of cardiac function.

Nature
Accurate assessment of cardiac function is crucial for the diagnosis of cardiovascular disease, screening for cardiotoxicity and decisions regarding the clinical management of patients with a critical illness. However, human assessment of cardiac fun...

Non-Standardized Patch-Based ECG Lead Together With Deep Learning Based Algorithm for Automatic Screening of Atrial Fibrillation.

IEEE journal of biomedical and health informatics
This study was to assess the feasibility of using non-standardized single-lead electrocardiogram (ECG) monitoring to automatically detect atrial fibrillation (AF) with special emphasis on the combination of deep learning based algorithm and modified ...

Segmentation and visualization of left atrium through a unified deep learning framework.

International journal of computer assisted radiology and surgery
PURPOSE: Left atrium segmentation and visualization serve as a fundamental and crucial role in clinical analysis and understanding of atrial fibrillation. However, most of the existing methods are directly transmitting information, which may cause re...