Attention-based residual improved U-Net model for continuous blood pressure monitoring by using photoplethysmography signal-Reference-Cited by-同舟云学术

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Attention-based residual improved U-Net model for continuous blood pressure monitoring by using photoplethysmography signal

Published:2022-05 Issue: Volume:75 Page:103581
ISSN:1746-8094
Container-title:Biomedical Signal Processing and Control
language:en
Short-container-title:Biomedical Signal Processing and Control

Author:

Yu Mingzheng,Huang Zhiwen,Zhu Yidan,Zhou Panyu,Zhu Jianmin

Funder

National Natural Science Foundation of China

University of Shanghai for Science and Technology

Publisher

Elsevier BV

Subject

Health Informatics,Signal Processing,Biomedical Engineering

Reference46 articles.

1. World health statistics 2016: monitoring health for the SDGs sustainable development goals;World Health Organization,2016

2. Recommendations for Blood Pressure Measurement in Humans and Experimental Animals

3. High blood pressure and cardiovascular disease;Fuchs;Hypertension,2020

4. Techniques for non-invasive monitoring of arterial blood pressure;Meidert;Front. Med.,2018

5. Perioperative continuous noninvasive arterial pressure monitoring for neuroendovascular interventions: prospective study for evaluation of the vascular unloading technique;Bugarini;World Neurosurg.,2021

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1. Continuous blood pressure monitoring based on transformer encoders and stacked attention gated recurrent units;Biomedical Signal Processing and Control;2025-01

2. Advancing cuffless blood pressure estimation: A PPG-based multi-task learning model for enhanced feature extraction and fusion;Biomedical Signal Processing and Control;2024-09

3. A Lightweight Convolutional Neural Network for Personalized Blood Pressure Estimation Based on Photoplethysmography;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

4. Integrating Transfer Learning with Scalogram Analysis for Blood Pressure Estimation from PPG Signals;2024-06-13

5. Prediction of arterial blood pressure waveforms based on Multi-Task learning;Biomedical Signal Processing and Control;2024-06

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