On Merging Feature Engineering and Deep Learning for Diagnosis, Risk Prediction and Age Estimation Based on the 12-Lead ECG-Reference-Cited by-同舟云学术

On Merging Feature Engineering and Deep Learning for Diagnosis, Risk Prediction and Age Estimation Based on the 12-Lead ECG

Published:2023-07 Issue:7 Volume:70 Page:2227-2236
ISSN:0018-9294
Container-title:IEEE Transactions on Biomedical Engineering
language:
Short-container-title:IEEE Trans. Biomed. Eng.

Author:

Zvuloni Eran¹^ORCID,Read Jesse²^ORCID,Ribeiro Antônio H.³^ORCID,Ribeiro Antonio Luiz P.⁴^ORCID,Behar Joachim A.⁵^ORCID

Affiliation:

1. Faculty of Biomedical Engineering, Technion-IIT, Israel

2. Computer Science Laboratory (LIX), École Polytechnique, Institut Polytechnique de Paris, France

3. Department of Information Technology, Uppsala University, Sweden

4. Department of Internal Medicine, Faculdade de Medicina, Telehealth Center, Hospital das Clínicas, Universidade Federal de Minas Gerais, Brazil

5. Faculty of Biomedical Engineering, Technion-IIT, Haifa, Israel

Funder

Technion EVPR Fund: Hittman Family Fund

Ministry of Health, Israel

Ministry of Science and Technology, Israel & Ministry of Europe and Foreign Affairs (MEAE) of Israel

Ministry of Higher Education, Research and Innovation (MESRI) of France

Israel PBC-VATAT

Technion Center for Machine Learning and Intelligent Systems

Israel Council of Higher Education

Israel Data Science Initiative

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Biomedical Engineering

Link

http://xplorestaging.ieee.org/ielx7/10/10155500/10025679.pdf?arnumber=10025679

Reference35 articles.

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2. 2016 ESC Guidelines for the management of atrial fibrillation developed in collaboration with EACTS

3. Deep neural network-estimated electrocardiographic age as a mortality predictor

4. Usefulness of P-Wave Duration and Morphologic Variability to Identify Patients Prone to Paroxysmal Atrial Fibrillation

5. Extended parabolic phase space mapping (EPPSM): Novel quadratic function for representation of heart rate variability signal;moharreri;Proc Comput Cardiol,0

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