The emergence of deep learning as the current state of art for classification and risk assessment of ventricular arrhythmias

Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

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Publication Details

Journal
The Journal of Physiology
Published
2024-09-01
DOI
https://doi.org/10.1113/jp287420
Citations
1
Primary Topic
Cardiac electrophysiology and arrhythmias
Type
article
Field-Weighted Citation Impact
0.40
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article

The emergence of deep learning as the current state of art for classification and risk assessment of ventricular arrhythmias

N. Amini
1 citations
The Journal of Physiology
Cardiac electrophysiology and arrhythmias
0.40
article

The emergence of deep learning as the current state of art for classification and risk assessment of ventricular arrhythmias

N. Amini
article en
1 citations

Abstract

Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

The Journal of PhysiologyVol. 602(18)
McGill University (CA)
Quality Education
Openalex Percentile: Top 33%
Cardiac electrophysiology and arrhythmias
0.40
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The emergence of deep learning as the current state of art for classification and risk assessment of ventricular arrhythmias — N. Amini · The Journal of Physiology (2024) | TGRS Research Map | TGRS