Literature DB >> 30261404

Arterial blood pressure feature estimation using photoplethysmography.

Armin Soltan Zadi1, Raichel Alex2, Rong Zhang3, Donald E Watenpaugh4, Khosrow Behbehani2.   

Abstract

Continuous and noninvasive monitoring of blood pressure has numerous clinical and fitness applications. Current methods of continuous measurement of blood pressure are either invasive and/or require expensive equipment. Therefore, we investigated a new method for the continuous estimation of two main features of blood pressure waveform: systolic and diastolic pressures. The estimates were obtained from a photoplethysmography signal as input to the fifth order autoregressive moving average models. The performance of the method was evaluated using beat-to-beat full-wave blood pressure measurements from 15 young subjects, with no known cardiovascular disorder, in supine position as they breathed normally and also while they performed a breath-hold maneuver. The level of error in the estimates, as measured by the root mean square of the model residuals, was less than 5 mmHg during normal breathing and less than 8 mmHg during the breath-hold maneuver. The mean of model residuals both during normal breathing and breath-hold maneuvers was considered to be less than 3.2 mmHg. The dependency of the accuracy of the estimates on the subject data was assessed by comparing the modeling errors for the 15 subjects. Less than 1% of the models showed significant differences (p < 0.05) from the other models, which indicates a high level of consistency among the models.
Copyright © 2018 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Autoregulation; Blood pressure; Estimation; Hemodynamics; Modeling; SaO2; Sleep disorders

Mesh:

Year:  2018        PMID: 30261404     DOI: 10.1016/j.compbiomed.2018.09.013

Source DB:  PubMed          Journal:  Comput Biol Med        ISSN: 0010-4825            Impact factor:   4.589


  5 in total

1.  Estimation of Arterial Blood Pressure Based on Artificial Intelligence Using Single Earlobe Photoplethysmography during Cardiopulmonary Resuscitation.

Authors:  Jong-Uk Park; Dong-Won Kang; Urtnasan Erdenebayar; Yoon-Ji Kim; Kyoung-Chul Cha; Kyoung-Joung Lee
Journal:  J Med Syst       Date:  2019-12-10       Impact factor: 4.460

2.  Estimating Blood Pressure from the Photoplethysmogram Signal and Demographic Features Using Machine Learning Techniques.

Authors:  Moajjem Hossain Chowdhury; Md Nazmul Islam Shuzan; Muhammad E H Chowdhury; Zaid B Mahbub; M Monir Uddin; Amith Khandakar; Mamun Bin Ibne Reaz
Journal:  Sensors (Basel)       Date:  2020-06-01       Impact factor: 3.576

3.  Genetic Deep Convolutional Autoencoder Applied for Generative Continuous Arterial Blood Pressure via Photoplethysmography.

Authors:  Muammar Sadrawi; Yin-Tsong Lin; Chien-Hung Lin; Bhekumuzi Mathunjwa; Shou-Zen Fan; Maysam F Abbod; Jiann-Shing Shieh
Journal:  Sensors (Basel)       Date:  2020-07-09       Impact factor: 3.576

Review 4.  Cuffless Single-Site Photoplethysmography for Blood Pressure Monitoring.

Authors:  Manish Hosanee; Gabriel Chan; Kaylie Welykholowa; Rachel Cooper; Panayiotis A Kyriacou; Dingchang Zheng; John Allen; Derek Abbott; Carlo Menon; Nigel H Lovell; Newton Howard; Wee-Shian Chan; Kenneth Lim; Richard Fletcher; Rabab Ward; Mohamed Elgendi
Journal:  J Clin Med       Date:  2020-03-07       Impact factor: 4.241

Review 5.  Assessing hemodynamics from the photoplethysmogram to gain insights into vascular age: a review from VascAgeNet.

Authors:  Peter H Charlton; Birutė Paliakaitė; Kristjan Pilt; Martin Bachler; Serena Zanelli; Dániel Kulin; John Allen; Magid Hallab; Elisabetta Bianchini; Christopher C Mayer; Dimitrios Terentes-Printzios; Verena Dittrich; Bernhard Hametner; Dave Veerasingam; Dejan Žikić; Vaidotas Marozas
Journal:  Am J Physiol Heart Circ Physiol       Date:  2021-12-24       Impact factor: 4.733

  5 in total

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