Literature DB >> 24110216

Noninvasive monitoring of blood pressure using optical Ballistocardiography and Photoplethysmograph approaches.

Zhihao Chen, Xiufeng Yang, Ju Teng Teo, Soon Huat Ng.   

Abstract

A new all optical method for long term and continuous blood pressure measurement and monitoring without using cuffs is proposed by using Ballistocardiography (BCG) and Photoplethysmograph (PPG). Based on BCG signal and PPG signal, a time delay between these two signals is obtained to calculate both systolic blood pressure and diastolic blood pressure via linear regression analysis. The fabricated noninvasive blood pressure monitoring device consists of a fiber sensor mat to measure BCG signal and a SpO2 sensor to measure PPG signal. A commercial digital oscillometric blood pressure meter is used to obtain reference values and for calibration. It has been found that by comparing with the reference device, our prototype has typical means and standard deviations of 9+/-5.6 mmHg for systolic blood pressure, 1.8+/-1.3 mmHg for diastolic blood pressure and 0.6+/-0.9 bpm for pulse rate, respectively. If the fiber optic SpO2 probe is used, this new all fiber cuffless noninvasive blood pressure monitoring device will truly be a MRI safe blood pressure measurement and monitoring device.

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Year:  2013        PMID: 24110216     DOI: 10.1109/EMBC.2013.6610029

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  8 in total

1.  A Pulse Rate Estimation Algorithm Using PPG and Smartphone Camera.

Authors:  Sarah Ali Siddiqui; Yuan Zhang; Zhiquan Feng; Anton Kos
Journal:  J Med Syst       Date:  2016-04-11       Impact factor: 4.460

2.  Simultaneous Monitoring of Ballistocardiogram and Photoplethysmogram Using a Camera.

Authors:  Dangdang Shao; Francis Tsow; Chenbin Liu; Yuting Yang; Nongjian Tao
Journal:  IEEE Trans Biomed Eng       Date:  2016-06-29       Impact factor: 4.538

3.  Toward Ubiquitous Blood Pressure Monitoring via Pulse Transit Time: Theory and Practice.

Authors:  Ramakrishna Mukkamala; Jin-Oh Hahn; Omer T Inan; Lalit K Mestha; Chang-Sei Kim; Hakan Töreyin; Survi Kyal
Journal:  IEEE Trans Biomed Eng       Date:  2015-06-05       Impact factor: 4.538

4.  Automated Detection of Hypertension Using Continuous Wavelet Transform and a Deep Neural Network with Ballistocardiography Signals.

Authors:  Jaypal Singh Rajput; Manish Sharma; T Sudheer Kumar; U Rajendra Acharya
Journal:  Int J Environ Res Public Health       Date:  2022-03-28       Impact factor: 3.390

5.  Non-Invasive Blood Pressure Estimation from ECG Using Machine Learning Techniques.

Authors:  Monika Simjanoska; Martin Gjoreski; Matjaž Gams; Ana Madevska Bogdanova
Journal:  Sensors (Basel)       Date:  2018-04-11       Impact factor: 3.576

Review 6.  The use of photoplethysmography for assessing hypertension.

Authors:  Mohamed Elgendi; Richard Fletcher; Yongbo Liang; Newton Howard; Nigel H Lovell; Derek Abbott; Kenneth Lim; Rabab Ward
Journal:  NPJ Digit Med       Date:  2019-06-26

Review 7.  Multimodal Photoplethysmography-Based Approaches for Improved Detection of Hypertension.

Authors:  Kaylie Welykholowa; Manish Hosanee; Gabriel Chan; 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:  2020-04-22       Impact factor: 4.241

8.  Calibration-Free Cuffless Blood Pressure Estimation Based on a Population With a Diverse Range of Age and Blood Pressure.

Authors:  Syunsuke Yamanaka; Koji Morikawa; Hiroshi Morita; Ji Young Huh; Osamu Yamamura
Journal:  Front Med Technol       Date:  2021-07-27
  8 in total

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