Deep learning for digitizing highly noisy paper-based ECG records-Reference-Cited by-同舟云学术

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Deep learning for digitizing highly noisy paper-based ECG records

Published:2020-12 Issue: Volume:127 Page:104077
ISSN:0010-4825
Container-title:Computers in Biology and Medicine
language:en
Short-container-title:Computers in Biology and Medicine

Author:

Li Yao,Qu Qixun,Wang Meng,Yu Liheng,Wang Jun,Shen Linghao,He Kunlun

Funder

National Key Research and Development Program of China

Publisher

Elsevier BV

Subject

Health Informatics,Computer Science Applications

Reference36 articles.

1. ST-segment elevation myocardial infarction;Vogel;Nature reviews Disease primers,2019

2. Evaluation and management of premature ventricular complexes;Marcus;Circulation,2020

3. Refining prediction of atrial fibrillation-related stroke using the P2-CHA2DS2-VASc score;Maheshwari;Circulation,2019

4. Recommendations for the Standardization and Interpretation of the Electrocardiogram

5. Advanced ECG in 2016: is there more than just a tracing?;Reichlin;Swiss Med Wkly,2016

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1. Paper-Recorded ECG Digitization Method with Automatic Reference Voltage Selection for Telemonitoring and Diagnosis;Diagnostics;2024-08-29

2. A Unified ECG Paper Digitization Framework with R-peak Detection for RR Interval Analysis and Deep ECG Learning Applications;2024 16th International Conference on Electronics, Computers and Artificial Intelligence (ECAI);2024-06-27

3. Impact of ECG data format on the performance of machine learning models for the prediction of myocardial infarction;Journal of Electrocardiology;2024-05

4. ECG-Image-Kit: a synthetic image generation toolbox to facilitate deep learning-based electrocardiogram digitization;Physiological Measurement;2024-05-01

5. Cardiac arrhythmia classification with rejection of ECG recordings based on uncertainty estimation from deep neural networks;Neural Computing and Applications;2023-12-09

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