
The Library
Featureless blood pressure estimation based on photoplethysmography signal using CNN and BiLSTM for IoT devices
Tools
Li, Yung-Hui, Harfiya, Latifa Nabila and Chang, Ching-Chun (2021) Featureless blood pressure estimation based on photoplethysmography signal using CNN and BiLSTM for IoT devices. Wireless Communications and Mobile Computing, 2021 . 9085100 . doi:10.1155/2021/9085100 ISSN 1530-8677.
|
PDF
WRAP-Featureless-blood-pressure-estimation-2021.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (888Kb) | Preview |
Official URL: https://doi.org/10.1155/2021/9085100
Abstract
Continuous blood pressure (BP) acquisition is critical to health monitoring of an individual. Photoplethysmography (PPG) is one of the most popular technologies in the last decade used for measuring blood pressure noninvasively. Several approaches have been carried out in various ways to utilize features extracted from PPG. In this study, we develop a continuous systolic and diastolic blood pressure (SBP and DBP) estimation mechanism without the need for any feature engineering. The raw PPG signal only got preprocessed before being fed to our model which mainly consists of one-dimensional convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) network. We evaluate the resulting SBP and DBP value by the root-mean-squared error (RMSE) and mean absolute error (MAE). This study addresses the effectiveness of the model by outperforming the previous feature engineering-based methods. We achieve RMSE of 11.503 and 6.525 for SBP and DBP, respectively, and MAE of 7.849 and 4.418 for SBP and DBP, respectively. The proposed method is expected to substantially enhance the current efficiency of healthcare IoT (Internet of Things) devices in BP monitoring using PPG signals only.
Item Type: | Journal Article | ||||||
---|---|---|---|---|---|---|---|
Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software Q Science > QP Physiology |
||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
SWORD Depositor: | Library Publications Router | ||||||
Library of Congress Subject Headings (LCSH): | Blood pressure , Blood pressure -- Measurement -- Computer simulation , Imaging systems in medicine, Medical informatics, Neural networks (Computer science) | ||||||
Journal or Publication Title: | Wireless Communications and Mobile Computing | ||||||
Publisher: | Hindawi | ||||||
ISSN: | 1530-8677 | ||||||
Official Date: | 26 November 2021 | ||||||
Dates: |
|
||||||
Volume: | 2021 | ||||||
Article Number: | 9085100 | ||||||
DOI: | 10.1155/2021/9085100 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||
Date of first compliant deposit: | 26 January 2022 | ||||||
Date of first compliant Open Access: | 26 January 2022 | ||||||
Related URLs: | |||||||
Contributors: |
|
Request changes or add full text files to a record
Repository staff actions (login required)
![]() |
View Item |
Downloads
Downloads per month over past year