The Library
Continuous blood pressure estimation using exclusively photopletysmography by LSTM-based signal-to-signal translation
Tools
Tools
Tools
Harfiya, Latifa Nabila, Chang, Ching-Chun and Li, Yung-Hui (2021) Continuous blood pressure estimation using exclusively photopletysmography by LSTM-based signal-to-signal translation. Sensors, 21 (9). e2952. doi:10.3390/s21092952 ISSN 1424-8220.
|
PDF
sensors-21-02952.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (4Mb) | Preview |
Official URL: https://doi.org/10.3390/s21092952
Abstract
Monitoring continuous BP signal is an important issue, because blood pressure (BP) varies over days, minutes, or even seconds for short-term cases. Most of photoplethysmography (PPG)-based BP estimation methods are susceptible to noise and only provides systolic blood pressure (SBP) and diastolic blood pressure (DBP) prediction. Here, instead of estimating a discrete value, we focus on different perspectives to estimate the whole waveform of BP. We propose a novel deep learning model to learn how to perform signal-to-signal translation from PPG to arterial blood pressure (ABP). Furthermore, using a raw PPG signal only as the input, the output of the proposed model is a continuous ABP signal. Based on the translated ABP signal, we extract the SBP and DBP values accordingly to ease the comparative evaluation. Our prediction results achieve average absolute error under 5 mmHg, with 70% confidence for SBP and 95% confidence for DBP without complex feature engineering. These results fulfill the standard from Association for the Advancement of Medical Instrumentation (AAMI) and the British Hypertension Society (BHS) with grade A. From the results, we believe that our model is applicable and potentially boosts the accuracy of an effective signal-to-signal continuous blood pressure estimation.
| Item Type: | Journal Article | ||||||
|---|---|---|---|---|---|---|---|
| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software Q Science > QP Physiology R Medicine > R Medicine (General) |
||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
| SWORD Depositor: | Library Publications Router | ||||||
| Library of Congress Subject Headings (LCSH): | Blood pressure -- Measurement -- Computer simulation, Imaging systems in medicine, Deep learning (Machine learning) | ||||||
| Journal or Publication Title: | Sensors | ||||||
| Publisher: | MDPI | ||||||
| ISSN: | 1424-8220 | ||||||
| Official Date: | 23 April 2021 | ||||||
| Dates: |
|
||||||
| Volume: | 21 | ||||||
| Number: | 9 | ||||||
| Article Number: | e2952 | ||||||
| DOI: | 10.3390/s21092952 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||
| Date of first compliant deposit: | 11 March 2022 | ||||||
| Date of first compliant Open Access: | 11 March 2022 | ||||||
| RIOXX Funder/Project Grant: |
|
||||||
| Is Part Of: | 1 |
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
Downloads
Downloads per month over past year
