eprintid: 16502
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/65/02
datestamp: 2023-12-19 03:23:01
lastmod: 2023-12-19 03:23:01
status_changed: 2023-12-19 03:06:22
type: article
metadata_visibility: show
creators_name: Ismail, S.N.A.
creators_name: Nayan, N.A.
creators_name: Jaafar, R.
creators_name: May, Z.
title: Recent Advances in Non-Invasive Blood Pressure Monitoring and Prediction Using a Machine Learning Approach
ispublished: pub
keywords: Acoustic wave velocity; Blood; Diagnosis; Machine learning; Monitoring; Signal processing; Wave propagation, Blood pressure monitoring device; Blood-pressure monitoring; Continuous monitoring; Health monitoring; Health status; Human health; Machine learning approaches; Machine-learning; Non-invasive; Pressure predictions, Blood pressure
note: cited By 9
abstract: Blood pressure (BP) monitoring can be performed either invasively via arterial catheterization or non-invasively through a cuff sphygmomanometer. However, for conscious individuals, traditional cuff-based BP monitoring devices are often uncomfortable, intermittent, and impractical for frequent measurements. Continuous and non-invasive BP (NIBP) monitoring is currently gaining attention in the human health monitoring area due to its promising potentials in assessing the health status of an individual, enabled by machine learning (ML), for various purposes such as early prediction of disease and intervention treatment. This review presents the development of a non-invasive BP measuring tool called sphygmomanometer in brief, summarizes state-of-the-art NIBP sensors, and identifies extended works on continuous NIBP monitoring using commercial devices. Moreover, the NIBP predictive techniques including pulse arrival time, pulse transit time, pulse wave velocity, and ML are elaborated on the basis of bio-signals acquisition from these sensors. Additionally, the different BP values (systolic BP, diastolic BP, mean arterial pressure) of the various ML models adopted in several reported studies are compared in terms of the international validation standards developed by the Advancement of Medical Instrumentation (AAMI) and the British Hypertension Society (BHS) for clinically-approved BP monitors. Finally, several challenges and possible solutions for the implementation and realization of continuous NIBP technology are addressed. © 2022 by the authors.
date: 2022
publisher: MDPI
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137678978&doi=10.3390%2fs22166195&partnerID=40&md5=85557ccd4e2852e4f81b68bfc7371b60
id_number: 10.3390/s22166195
full_text_status: none
publication: Sensors
volume: 22
number: 16
refereed: TRUE
issn: 14248220
citation:   Ismail, S.N.A. and Nayan, N.A. and Jaafar, R. and May, Z.  (2022) Recent Advances in Non-Invasive Blood Pressure Monitoring and Prediction Using a Machine Learning Approach.  Sensors, 22 (16).   ISSN 14248220