Automatic Classification of Full- and Reduced-Lead Electrocardiograms Using Morphological Feature Extraction-Reference-Cited by-同舟云学术

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Automatic Classification of Full- and Reduced-Lead Electrocardiograms Using Morphological Feature Extraction

Published:2021-09-13 Issue: Volume: Page:
ISSN:
Container-title:2021 Computing in Cardiology (CinC)
language:
Short-container-title:

Author:

Hammer Alexander,Scherpf Matthieu,Ernst Hannes,Weiss Jonas,Schwensow Daniel,Schmidt Martin

Funder

European Union's Horizon 2020 research and innovation program

European Regional Development Fund

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9662654/9662645/09662797.pdf?arnumber=9662797

Reference13 articles.

1. Iterative two-dimensional signal warping—Towards a generalized approach for adaption of one-dimensional signals

2. Two-Dimensional Warping for One-Dimensional Signals—Conceptual Framework and Application to ECG Processing

3. T Wave Amplitude Correction of QT Interval Variability for Improved Repolarization Lability Measurement

4. An open-source framework for stress-testing non-invasive foetal ECG extraction algorithms

5. Causability and explainability of artificial intelligence in medicine

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Fusion of automatically learned rhythm and morphology features matches diagnostic criteria and enhances AI explainability;2024-08-23

2. xECGArch: a trustworthy deep learning architecture for interpretable ECG analysis considering short-term and long-term features;Scientific Reports;2024-06-07

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