An efficient Blood pressure estimation algorithm based on machine learning using a mixture of non-contact hand-face PPG signals features-Reference-Cited by-同舟云学术

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An efficient Blood pressure estimation algorithm based on machine learning using a mixture of non-contact hand-face PPG signals features

Published:2024-07-11 Issue: Volume: Page:59-64
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Container-title:2024 IEEE 7th International Conference on Advanced Technologies, Signal and Image Processing (ATSIP)
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Author:

Mbarki Zouhair¹,Amri Yessine²,Slama Amine Ben³,Trabelsi Hedi³,Seddik Hassene¹

Affiliation:

1. ENSIT University of Tunis,RIFTSI Research Laboratory,Tunis,Tunisia

2. Bechir Hamza Children’s Hospital, Bab Saadoun Square,Biochemistry Laboratory,Tunis,Tunisia,1007

3. University of Tunis ELmanar, Higher Institute of Medical Technologies (ISTMT), Laboratory of Biophysics and Medical Technologies, LR13ES07 (BTM),Tunis,Tunisia

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10638790/10638839/10639021.pdf?arnumber=10639021

Reference18 articles.

1. A novel interpretable feature set optimization method in blood pressure estimation using photoplethysmography signals

2. BPNet: A multi-modal fusion neural network for blood pressure estimation using ECG and PPG

3. Causal inference based cuffless blood pressure estimation: A pilot study

4. Nonlinear features of photoplethysmography signals for Non-invasive blood pressure estimation

5. Comparison of pulse rate variability and morphological features of photoplethysmograms in estimation of blood pressure

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