Hybrid D1DCnet Using Forehead iPPG for Continuous and Noncontact Blood Pressure Measurement
Author:
Affiliation:
1. Institute of AI and Robotics, Academy for Engineering and Technology, Fudan University, Shanghai, China
2. Jihua Laboratory, Foshan, China
3. School of Basic Medicine, Guangzhou University of Chinese Medicine, Guangzhou, China
Funder
National Key Research and Development Program of China
Key Area Support Plan of Guangdong Province for the Jihua Laboratory
Special Projects in Key Fields of the Department of Education of Guangdong Province
National Natural Science Foundation of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/7361/10032973/09997532.pdf?arnumber=9997532
Reference44 articles.
1. Repetitive neural network (RNN) based blood pressure estimation using PPG and ECG signals
2. Breaking the softmax bottleneck: A high-rank RNN language model;yang;arXiv 1711 03953,2017
3. Blood Pressure Estimation from Photoplethysmogram Using a Spectro-Temporal Deep Neural Network
4. A pattern recognition approach to understanding the multi-layer perception
5. A multistage deep neural network model for blood pressure estimation using photoplethysmogram signals
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