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Atrial Fibrillation

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A new machine learning approach for predicting likelihood of recurrence following ablation for atrial fibrillation from CT.

BMC medical imaging
OBJECTIVE: To investigate left atrial shape differences on CT scans of atrial fibrillation (AF) patients with (AF+) versus without (AF-) post-ablation recurrence and whether these shape differences predict AF recurrence.

Deep Neural Networks Can Predict New-Onset Atrial Fibrillation From the 12-Lead ECG and Help Identify Those at Risk of Atrial Fibrillation-Related Stroke.

Circulation
BACKGROUND: Atrial fibrillation (AF) is associated with substantial morbidity, especially when it goes undetected. If new-onset AF could be predicted, targeted screening could be used to find it early. We hypothesized that a deep neural network could...

An ECG Signal Classification Method Based on Dilated Causal Convolution.

Computational and mathematical methods in medicine
The incidence of cardiovascular disease is increasing year by year and is showing a younger trend. At the same time, existing medical resources are tight. The automatic detection of ECG signals becomes increasingly necessary. This paper proposes an a...

Artificial intelligence for a personalized diagnosis and treatment of atrial fibrillation.

American journal of physiology. Heart and circulatory physiology
Although atrial fibrillation (AF) is the most common cardiac arrhythmia, its early identification, diagnosis, and treatment is still challenging. Due to its heterogeneous mechanisms and risk factors, targeting an individualized treatment of AF demand...

A Review on the State of the Art in Atrial Fibrillation Detection Enabled by Machine Learning.

IEEE reviews in biomedical engineering
Atrial Fibrillation (AF) the most commonly occurring type of cardiac arrhythmia is one of the main causesĀ of morbidity and mortality worldwide. The timely diagnosis of AF is an equally important and challenging task because of its asymptomatic and ep...

Over-fitting suppression training strategies for deep learning-based atrial fibrillation detection.

Medical & biological engineering & computing
Nowadays, deep learning-based models have been widely developed for atrial fibrillation (AF) detection in electrocardiogram (ECG) signals. However, owing to the inevitable over-fitting problem, classification accuracy of the developed models severely...

An IoT and Fog Computing-Based Monitoring System for Cardiovascular Patients with Automatic ECG Classification Using Deep Neural Networks.

Sensors (Basel, Switzerland)
Telemedicine and all types of monitoring systems have proven to be a useful and low-cost tool with a high level of applicability in cardiology. The objective of this work is to present an IoT-based monitoring system for cardiovascular patients. The s...

Explainable artificial intelligence to detect atrial fibrillation using electrocardiogram.

International journal of cardiology
INTRODUCTION: Early detection and intervention of atrial fibrillation (AF) is a cornerstone for effective treatment and prevention of mortality. Diverse deep learning models (DLMs) have been developed, but they could not be applied in clinical practi...