AI-Based Stroke Disease Prediction System Using ECG and PPG Bio-Signals-Reference-Cited by-同舟云学术

AI-Based Stroke Disease Prediction System Using ECG and PPG Bio-Signals

Published:2022 Issue: Volume:10 Page:43623-43638
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Yu Jaehak¹,Park Sejin²^ORCID,Kwon Soon-Hyun¹,Cho Kang-Hee³,Lee Hansung⁴^ORCID

Affiliation:

1. Department of Knowledge-Converged Super Brain (KSB) Convergence Research, Electronics and Telecommunications Research Institute, Daejeon, South Korea

2. Safety Measurement Institute, Korea Research Institute of Standards and Science, Daejeon, South Korea

3. Department of Rehabilitation Medicine, College of Medicine, Chungnam National University, Daejeon, South Korea

4. School of Creative Convergence, Andong National University, Andong, South Korea

Funder

National Research Council of Science and Technology (NST) grant

Korean Government [Ministry of Science, ICT and Future Planning (MSIP)]

Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry (IPET) through Smart Farm Innovation Technology Development Program, funded

Ministry of Agriculture, Food and Rural Affairs (MAFRA) and Rural Development Administration (RDA) and Ministry of Science and ICT

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09761215.pdf?arnumber=9761215

Reference60 articles.

1. Noninvasive Assessment of Arterial Stiffness and Risk of Atherosclerotic Events

2. Photoplethysmography and its application in clinical physiological measurement

3. ECG-based heartbeat classification for arrhythmia detection: A survey

4. A deep learning approach for ECG-based heartbeat classification for arrhythmia detection

5. Improvements in atrial fibrillation detection for real-time monitoring

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