Atrial Fibrillation Detection
14 papers with code • 2 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in Atrial Fibrillation Detection
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Datasets
AF Classification from a Short Single Lead ECG Recording - The PhysioNet Computing in Cardiology Challenge 2017
IRIDIA-AF
Latest papers with no code
Log-Spectral Matching GAN: PPG-based Atrial Fibrillation Detection can be Enhanced by GAN-based Data Augmentation with Integration of Spectral Loss
Photoplethysmography (PPG) is a ubiquitous physiological measurement that detects beat-to-beat pulsatile blood volume changes and hence has a potential for monitoring cardiovascular conditions, particularly in ambulatory settings.
SCRIB: Set-classifier with Class-specific Risk Bounds for Blackbox Models
Despite deep learning (DL) success in classification problems, DL classifiers do not provide a sound mechanism to decide when to refrain from predicting.
HAN-ECG: An Interpretable Atrial Fibrillation Detection Model Using Hierarchical Attention Networks
The cardiologist level performance in detecting this arrhythmia is often achieved by deep learning-based methods, however, they suffer from the lack of interpretability.
A Novel Deep Arrhythmia-Diagnosis Network for Atrial Fibrillation Classification Using Electrocardiogram Signals
Atrial fibrillation (AF), a common abnormal heartbeat rhythm, is a life-threatening recurrent disease that affects older adults.
Atrial Fibrillation Detection Using Deep Features and Convolutional Networks
The first approach used a pretrained DenseNet model to extract features that were then classified using Support Vector Machines, and the second approach used the spectrograms as direct input into a convolutional network.
ECGNET: Learning where to attend for detection of atrial fibrillation with deep visual attention
The complexity of the patterns associated with Atrial Fibrillation (AF) and the high level of noise affecting these patterns have significantly limited the current signal processing and shallow machine learning approaches to get accurate AF detection results.
Kalman-based Spectro-Temporal ECG Analysis using Deep Convolutional Networks for Atrial Fibrillation Detection
In this article, we propose a novel ECG classification framework for atrial fibrillation (AF) detection using spectro-temporal representation (i. e., time varying spectrum) and deep convolutional networks.
Automatic Detection of Atrial Fibrillation Based on Continuous Wavelet Transform and 2D Convolutional Neural Networks
The proposed method analyzed the time-frequency features of the electrocardiogram (ECG), thus being different to conventional AF detecting methods that implement isolating atrial or ventricular activities.
A Simple Probabilistic Model for Uncertainty Estimation
The article focuses on determining the predictive uncertainty of a model on the example of atrial fibrillation detection problem by a single-lead ECG signal.
Automatic online detection of atrial fibrillation based on symbolic dynamics and Shannon entropy
Results Four publicly-accessible sets of clinical data (Long-Term AF, MIT-BIH AF, MIT-BIH Arrhythmia, and MIT-BIH Normal Sinus Rhythm Databases) were selected for investigation.
