@inproceedings{777e659eb6334a1abc18e8cb769e02f5,
title = "Dual-site Photoplethysmography Sensing for Noninvasive Continuous-time Blood Pressure Monitoring Using Artificial Neural Network",
abstract = "In this work, we propose dual site Photoplethysmography (PPG) sensing for blood pressure monitoring using Artificial Neural Network (ANN). The method was implemented on a microcontroller for real-time BP monitoring. The models were evaluated on 15 volunteers and the ANN model achieved a MAE±SD 0.29 ± 4.49 mmHg for SBP and 0.5±2.4 mmHg for DBP. The proposed dual PPG site ANN model exhibited superior performance and robustness in real-time tests compared to the classical ANN single-site PPG model.",
keywords = "Blood Pressure, Linear regression, Neural Network, Photoplethysmography, pulse wave velocity",
author = "Anas Rababah and Khan, {Moien A.B.} and Falah Awwad and Mohamed Atef",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE Biomedical Circuits and Systems Conference, BioCAS 2022 ; Conference date: 13-10-2022 Through 15-10-2022",
year = "2022",
doi = "10.1109/BioCAS54905.2022.9948549",
language = "English",
series = "BioCAS 2022 - IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Systems for a Better Future, Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "462--466",
booktitle = "BioCAS 2022 - IEEE Biomedical Circuits and Systems Conference",
}