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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.
PhysioZoo: The Open Digital Physiological Biomarkers Resource
PhysioZoo is a collaborative platform designed for the analysis of continuous physiological time series.
Machine Learning for Ranking f-wave Extraction Methods in Single-Lead ECGs
The approach is well-suited for processing large Holter data sets annotated with respect to the presence of AF.
Estimation of f-wave Dominant Frequency Using a Voting Scheme
Using these three algorithms in a voting scheme, the classifier obtained AUC=0. 60 and the DAFs were mostly spread around 6 Hz, 5. 66 (4. 83-7. 47).
ArNet-ECG: Deep Learning for the Detection of Atrial Fibrillation from the Raw Electrocardiogram
We further hypothesize that the performance reached leveraging the raw ECG will be superior to previously developed methods using the beat-to-beat interval variation time series.
Generalizable and Robust Deep Learning Algorithm for Atrial Fibrillation Diagnosis Across Ethnicities, Ages and Sexes
This retrospective study is, to the best of our knowledge, the first to develop and assess the generalization performance of a deep learning (DL) model for AF events detection from long term beat-to-beat intervals across ethnicities, ages and sexes.
Remote atrial fibrillation burden estimation using deep recurrent neural network
The generalizations of ArNet and XGB were also evaluated on the independent test PhysioNet LTAF database.
