A Physics-Guided Deep Learning Approach for Functional Assessment of Cardiovascular Disease in IoT-Based Smart Health

Author:

Zhang Dong1ORCID,Liu Xiujian1ORCID,Xia Jun2,Gao Zhifan1ORCID,Zhang Heye1ORCID,de Albuquerque Victor Hugo C.3ORCID

Affiliation:

1. School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, China

2. Department of Radiology, Shenzhen Second People’s Hospital, Shenzhen, China

3. Department of Teleinformatics Engineering, Federal University of Ceará, Fortaleza, Brazil

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Guangdong Natural Science Funds for Distinguished Young Scholar

National Youth Talent Support Program

National Council for Scientific and Technological Development

Project of Shenzhen Basic Development Project

Basic and Applied Basic Research Foundation of Guangdong Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Reference59 articles.

1. 2018 ESC/EACTS guidelines on myocardial revascularization. The task force on myocardial revascularization of the European society of cardiology (ESC) and European association for cardio-thoracic surgery (EACTS);neumann;Giornale Italiano di Cardiologia,2019

2. SCCT guidelines for the interpretation and reporting of coronary CT angiography: A report of the Society of Cardiovascular Computed Tomography Guidelines Committee

3. Experimental basis of determining maximum coronary, myocardial, and collateral blood flow by pressure measurements for assessing functional stenosis severity before and after percutaneous transluminal coronary angioplasty.

4. WaveNet: A generative model for raw audio;van den oord;arXiv 1609 03499,2016

5. Validation of a Doppler guide wire for intravascular measurement of coronary artery flow velocity.

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