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
Deciphering Heartbeat Signatures: A Vision Transformer Approach to Explainable Atrial Fibrillation Detection from ECG Signals
These models are applied to the Chapman-Shaoxing dataset to classify atrial fibrillation, as well as another common arrhythmia, sinus bradycardia, and normal sinus rhythm heartbeats.
RawECGNet: Deep Learning Generalization for Atrial Fibrillation Detection from the Raw ECG
Methods: To address this limitation, we have developed a deep learning model, named RawECGNet, to detect episodes of AF and atrial flutter (AFl) using the raw, single-lead ECG.
A Novel 1D Generative Adversarial Network-based Framework for Atrial Fibrillation Detection using Restored Wrist Photoplethysmography Signals
Self-AFNet managed to achieve an accuracy of 98. 07% and 98. 97%, respectively using two ECG splits which is comparable to the performance of AF detection utilizing reconstructed PPG segments.
Photoplethysmography based atrial fibrillation detection: an updated review from July 2019
This paper offers a comprehensive review of the latest advancements in PPG-based AF detection, utilizing digital health and artificial intelligence (AI) solutions, within the timeframe spanning from July 2019 to December 2022.
PPG-to-ECG Signal Translation for Continuous Atrial Fibrillation Detection via Attention-based Deep State-Space Modeling
Here, we propose a subject-independent attention-based deep state-space model to translate PPG signals to corresponding ECG waveforms.
Compressor-Based Classification for Atrial Fibrillation Detection
We achieved good classification results while learning on the full MIT-BIH Atrial Fibrillation database, close to the best specialized AF detection algorithms (avg.
Benchmarking the Impact of Noise on Deep Learning-based Classification of Atrial Fibrillation in 12-Lead ECG
We analyze the accuracy of the Deep Learning model with respect to both metrics and observe that the method can robustly identify atrial fibrillation, even in cases signals are labelled by human experts as being noisy on multiple leads.
Atrial Fibrillation Detection Using RR-Intervals for Application in Photoplethysmographs
Our primary goal is to analyze Atrial Fibrillation data within ECGs to develop a model based only on RR-Intervals, or the length between heart-beats, to create a real time classification model for Atrial Fibrillation to be implemented in common heart-rate monitors on the market today.
Atrial Fibrillation Detection Using Weight-Pruned, Log-Quantised Convolutional Neural Networks
Deep neural networks (DNN) are a promising tool in medical applications.
Non-contact Atrial Fibrillation Detection from Face Videos by Learning Systolic Peaks
Results: Our proposed method can accurately extract systolic peaks from face videos for AF detection.
