Atrial fibrillation signatures on intracardiac electrograms identified by deep learning
Atrial fibrillation signatures on intracardiac electrograms identified by deep learning
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United States: Elsevier Ltd
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Language
English
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Publisher
United States: Elsevier Ltd
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AbstractBackgroundAutomatic detection of atrial fibrillation (AF) by cardiac devices is increasingly common yet suboptimally groups AF, flutter or tachycardia (AT) together as ‘high rate events’. This may delay or misdirect therapy. ObjectiveWe hypothesized that deep learning (DL) can accurately classify AF from AT by revealing electrogram (EGM) si...
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Full title
Atrial fibrillation signatures on intracardiac electrograms identified by deep learning
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TN_cdi_proquest_miscellaneous_2651693002
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_miscellaneous_2651693002
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ISSN
0010-4825
E-ISSN
1879-0534
DOI
10.1016/j.compbiomed.2022.105451
