An End-to-End Cardiac Arrhythmia Recognition Method with an Effective DenseNet Model on Imbalanced D...
An End-to-End Cardiac Arrhythmia Recognition Method with an Effective DenseNet Model on Imbalanced Datasets Using ECG Signal
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Publisher
New York: Hindawi
Journal title
Language
English
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Publisher
New York: Hindawi
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Scope and Contents
Contents
Electrocardiography (ECG) is a well-known noninvasive technique in medical science that provides information about the heart’s rhythm and current conditions. Automatic ECG arrhythmia diagnosis relieves doctors’ workload and improves diagnosis effectiveness and efficiency. This study proposes an automatic end-to-end 2D CNN (two-dimensional convoluti...
Alternative Titles
Full title
An End-to-End Cardiac Arrhythmia Recognition Method with an Effective DenseNet Model on Imbalanced Datasets Using ECG Signal
Authors, Artists and Contributors
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Record Identifier
TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_9536938
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_9536938
Other Identifiers
ISSN
1687-5265
E-ISSN
1687-5273
DOI
10.1155/2022/9475162
