@inproceedings{985192fa316f4337aec0718c3a12b906,
title = "Estimating Blood Pressure via Artificial Neural Networks Based on Measured Photoplethysmography Waveforms",
abstract = "A new approach for estimating blood pressure from photoplethysmography (PPG) signals is developed using artificial neural networks (ANNs). Blood Pressure is one of the most important parameters that can provide valuable information of personal healthcare. A reflective photoplethysmography (PPG) sensor module is developed for the cuffless, non-invasive blood pressure (BP) measurement based on PPG at wrist on radial artery. Blood Pressure is in a relation with the pulse duration of the PPG. In this paper, we propose to estimate blood pressure from PPG signal by using artificial neural networks approach. This is the first reported study to consider varied temporal periods of PPG waveforms as features for application of artificial neural networks (ANNs) to estimate blood pressure. We compared our results with those measured using a commercial cuff-based digital blood pressure measuring device and obtained encouraging results of overall SBP and DBP regression (R) as 0.99115.",
keywords = "Artificial Neural Networks (ANN), Blood Pressure (BP) Measurement, PPG Sensor",
author = "Priyanka, {K. N.G.} and Chao, {Paul C.-P.} and Tu, {Tse Yi} and Kao, {Yung Hua} and Yeh, {Ming Hua} and Rajeev Pandey and Eka, {Fitrah P.}",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 17th IEEE SENSORS Conference, SENSORS 2018 ; Conference date: 28-10-2018 Through 31-10-2018",
year = "2018",
month = oct,
day = "28",
doi = "10.1109/ICSENS.2018.8589796",
language = "English",
series = "Proceedings of IEEE Sensors",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2018 IEEE SENSORS, SENSORS 2018 - Conference Proceedings",
address = "美國",
}