Issues in the automated classification of multilead ECGs using heterogeneous labels and populations
Objective. The standard twelve-lead electrocardiogram (ECG) is a widely used tool for
monitoring cardiac function and diagnosing cardiac disorders. The development of smaller …
monitoring cardiac function and diagnosing cardiac disorders. The development of smaller …
[HTML][HTML] Computer-Interpreted Electrocardiograms: Impact on Cardiology Practice
SciELO - Brasil - Computer-Interpreted Electrocardiograms: Impact on Cardiology Practice
Computer-Interpreted Electrocardiograms: Impact on Cardiology Practice Acessibilidade …
Computer-Interpreted Electrocardiograms: Impact on Cardiology Practice Acessibilidade …
Enhanced multi-label cardiology diagnosis with channel-wise recurrent fusion
W Wen, H Zhang, Z Wang, X Gao, P Wu, J Lin… - Computers in Biology …, 2024 - Elsevier
The timely detection of abnormal electrocardiogram (ECG) signals is vital for preventing
heart disease. However, traditional automated cardiology diagnostic methods have the …
heart disease. However, traditional automated cardiology diagnostic methods have the …
A comprehensive review on efficient artificial intelligence models for classification of abnormal cardiac rhythms using electrocardiograms
Deep learning has made many advances in data classification using electrocardiogram
(ECG) waveforms. Over the past decade, data science research has focused on developing …
(ECG) waveforms. Over the past decade, data science research has focused on developing …
Visual interpretation of deep learning model in ECG classification: A comprehensive evaluation of feature attribution methods
Feature attribution methods can visually highlight specific input regions containing influential
aspects affecting a deep learning model's prediction. Recently, the use of feature attribution …
aspects affecting a deep learning model's prediction. Recently, the use of feature attribution …
A systematic survey of data augmentation of ECG signals for AI applications
AI techniques have recently been put under the spotlight for analyzing electrocardiograms
(ECGs). However, the performance of AI-based models relies on the accumulation of large …
(ECGs). However, the performance of AI-based models relies on the accumulation of large …
心电领域中的自监督学习方法综述.
韩涵， 黄训华， 常慧慧， 樊好义… - Journal of Frontiers of …, 2024 - search.ebscohost.com
深度学习因其强大的数据表征能力已被广泛应用于心电(ECG) 信号分析领域,
但有监督方法的训练过程需要大量标签, 而心电数据标注通常是耗时且成本高昂的 …
但有监督方法的训练过程需要大量标签, 而心电数据标注通常是耗时且成本高昂的 …
Arrhythmia classification of 12-lead and reduced-lead electrocardiograms via recurrent networks, scattering, and phase harmonic correlation
PA Warrick, V Lostanlen, M Eickenberg… - Physiological …, 2022 - iopscience.iop.org
We describe an automatic classifier of arrhythmias based on 12-lead and reduced-lead
electrocardiograms. Our classifier comprises four modules: scattering transform (ST), phase …
electrocardiograms. Our classifier comprises four modules: scattering transform (ST), phase …
[PDF][PDF] Classification of electrocardiogram signals based on federated learning and a gaussian multivariate aggregation module
MN Meqdad, AH Hussein, SO Husain… - Indonesian Journal of …, 2023 - researchgate.net
Categorization of cardiac abnormalities received from several centers is not possible within
the quickest time because of privacy and security restrictions. Today, individuals' security …
the quickest time because of privacy and security restrictions. Today, individuals' security …
Self-supervised learning for atrial fibrillation detection with ECG using CNNTransformer
Cardiovascular diseases are a significant cause of mortality worldwide, and the accurate
diagnosis of these conditions is essential for effective treatment and management …
diagnosis of these conditions is essential for effective treatment and management …