Computer-aided diagnosis of atrial fibrillation based on ECG Signals: A review
Arrhythmia is a type of disorder that affects the pattern and rate of the heartbeat. Among the
various arrhythmia conditions, atrial fibrillation (AF) is the most prevalent. AF is associated …
various arrhythmia conditions, atrial fibrillation (AF) is the most prevalent. AF is associated …
Screening strategies for atrial fibrillation: a systematic review and cost-effectiveness analysis
BACKGROUND: Atrial fibrillation (AF) is a common cardiac arrhythmia that increases the risk
of thromboembolic events. Anticoagulation therapy to prevent AF-related stroke has been …
of thromboembolic events. Anticoagulation therapy to prevent AF-related stroke has been …
Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network
Computerized electrocardiogram (ECG) interpretation plays a critical role in the clinical ECG
workflow. Widely available digital ECG data and the algorithmic paradigm of deep learning …
workflow. Widely available digital ECG data and the algorithmic paradigm of deep learning …
AF classification from a short single lead ECG recording: The PhysioNet/computing in cardiology challenge 2017
The PhysioNet/Computing in Cardiology (CinC) Challenge 2017 focused on differentiating
AF from noise, normal or other rhythms in short term (from 9-61 s) ECG recordings …
AF from noise, normal or other rhythms in short term (from 9-61 s) ECG recordings …
Detecting atrial fibrillation by deep convolutional neural networks
Background Atrial fibrillation (AF) is the most common cardiac arrhythmia. The incidence of
AF increases with age, causing high risks of stroke and increased morbidity and mortality …
AF increases with age, causing high risks of stroke and increased morbidity and mortality …
Automated detection of atrial fibrillation using long short-term memory network with RR interval signals
Atrial Fibrillation (AF), either permanent or intermittent (paroxysnal AF), increases the risk of
cardioembolic stroke. Accurate diagnosis of AF is obligatory for initiation of effective …
cardioembolic stroke. Accurate diagnosis of AF is obligatory for initiation of effective …
A novel data augmentation method to enhance deep neural networks for detection of atrial fibrillation
P Cao, X Li, K Mao, F Lu, G Ning, L Fang… - … Signal Processing and …, 2020 - Elsevier
Automated detection of atrial fibrillation (AF) from electrocardiogram (ECG) recordings
remains challenging in real clinical settings. Deep neural networks (DNN) emerge as a …
remains challenging in real clinical settings. Deep neural networks (DNN) emerge as a …
[HTML][HTML] Detection of atrial fibrillation using 1D convolutional neural network
CH Hsieh, YS Li, BJ Hwang, CH Hsiao - Sensors, 2020 - mdpi.com
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 …
embolic stroke. Most of the existing AF detection methods usually convert 1D time-series …
HAN-ECG: An interpretable atrial fibrillation detection model using hierarchical attention networks
Atrial fibrillation (AF) is one of the most prevalent cardiac arrhythmias that affects the lives of
many people around the world and is associated with a five-fold increased risk of stroke and …
many people around the world and is associated with a five-fold increased risk of stroke and …
Multi-domain modeling of atrial fibrillation detection with twin attentional convolutional long short-term memory neural networks
Atrial fibrillation (AF) is a common arrhythmia, and its incidence increases with age. Many
methods have been developed to identify AF, including both the hand-picked features by …
methods have been developed to identify AF, including both the hand-picked features by …