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Found 6 papers, 1 papers with code
Weakly Supervised Arrhythmia Detection Based on Deep Convolutional Neural Network
The results show that the models achieve beat-level accuracies of 99. 09% in detecting atrial fibrillation, and 99. 13% in detecting morphological arrhythmias, which are comparable to that of fully supervised learning models, demonstrating their effectiveness.
Automatic Detection of ECG Abnormalities by using an Ensemble of Deep Residual Networks with Attention
Heart disease is one of the most common diseases causing morbidity and mortality.
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.
Ranked #2 on
Atrial Fibrillation Detection
on MIT-BIH AF
VoxelAtlasGAN: 3D Left Ventricle Segmentation on Echocardiography with Atlas Guided Generation and Voxel-to-voxel Discrimination
And cGAN advantageously fuses substantial 3D spatial context information from 3D echocardiography by self-learning structured loss; 2) For the first time, it embeds the atlas into an end-to-end optimization framework, which uses 3D LV atlas as a powerful prior knowledge to improve the inference speed, address the lower contrast and the limited annotation problems of 3D echocardiography; 3) It combines traditional discrimination loss and the new proposed consistent constraint, which further improves the generalization of the proposed framework.
Multi-views Fusion CNN for Left Ventricular Volumes Estimation on Cardiac MR Images
Methods: In this paper, we propose a direct volumes prediction method based on the end-to-end deep convolutional neural networks (CNN).
Detecting atrial fibrillation by deep convolutional neural networks
Conclusion The proposed method using deep convolutional neural networks shows high sensitivity, specificity and accuracy, and, therefore, is a valuable tool for AF detection.
