A new deep learning model for assisted diagnosis on electrocardiogram-Reference-Cited by-同舟云学术

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A new deep learning model for assisted diagnosis on electrocardiogram

Published:2019 Issue:4 Volume:16 Page:2481-2491
ISSN:1547-1063
Container-title:Mathematical Biosciences and Engineering
language:en
Short-container-title:

Author:

Ke Wang Eric, ,Xi liu,Sun Ruipei,Wang Fan,Pan Leyun,Cheng Caixia,D. Dimitrakpoulou-Srauss Antonia,Zhe Nie,Li Yueping, ,

Publisher

American Institute of Mathematical Sciences (AIMS)

Subject

Applied Mathematics,Computational Mathematics,General Agricultural and Biological Sciences,Modelling and Simulation,General Medicine

Reference3 articles.

1. K. Mahantapas, M. Nasipuri and D. K. Basu, Knowledge-based ECG

2. D. E. Lake and J. R. Moorman, Accurate estimation of entropy in very short physiological time

3. H. Yuki, F. Hamido and O. S. Lih, et al., Computer-aided diagnosis of atrial fibrillation based on ECG

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1. AttBiLFNet: A novel hybrid network for accurate and efficient arrhythmia detection in imbalanced ECG signals;Mathematical Biosciences and Engineering;2024

2. Review on Advent of Artificial Intelligence in Electrocardiogram for the Detection of Extra-Cardiac and Cardiovascular Disease;IEEE Canadian Journal of Electrical and Computer Engineering;2023

3. A dynamic learning-based ECG feature extraction method for myocardial infarction detection;Physiological Measurement;2022-12-30

4. Convolutional Neural Network-Based ECG-Assisted Diagnosis for Coal Workers;International Journal of Environmental Research and Public Health;2022-12-20

5. A systematic review and Meta-data analysis on the applications of Deep Learning in Electrocardiogram;Journal of Ambient Intelligence and Humanized Computing;2022-07-07

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