Literature DB >> 28269029

Noninvasive and continuous blood pressure measurement via superficial temporal artery tonometry.

Julia Canning, Kendall Helbert, Grigoriy Iashin, Jonathan Matthews, Jason Yang, Margaret K Delano, Charles G Sodini.   

Abstract

The measurement of blood pressure is an important cardiovascular health assessment, yet the current set of methodologies is limited in resolution, repeatability, accuracy, simplicity, and safety. This paper presents the design and prototype implementation of a novel and easy-to-use medical device for noninvasive and continuous blood pressure monitoring through tonometry at the superficial temporal artery (STA). The device features a stable form factor inspired by over-ear headphones that adjusts easily from person to person using a combination prismatic and rotational joint. A stepper motor and pressure sensor, built into the device, apply a controlled force to flatten the artery and measure the wearer's blood pressure. The design is fully wireless, using Bluetooth communication to connect to a custom control and monitoring interface on the user's laptop that allows for easy calibration and real-time measurement. Preliminary testing of the device showed a percentage error from a blood pressure cuff mean arterial pressure measurement of 7.7% (7.0 mmHg). This was also compared to a Nexfin vascular unloading device, which showed a percentage error from the blood pressure cuff of 7.3% (6.6 mmHg).

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Year:  2016        PMID: 28269029     DOI: 10.1109/EMBC.2016.7591453

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


  4 in total

1.  Extraction and Evaluation of Discriminative Indexes of the Wearing Condition for High-Precision Blood Pressure Pulse Wave Measurement.

Authors:  Yosuke Osawa; Tetsuji Dohi
Journal:  Micromachines (Basel)       Date:  2022-04-27       Impact factor: 3.523

2.  Machine Learning and Electrocardiography Signal-Based Minimum Calculation Time Detection for Blood Pressure Detection.

Authors:  Majid Nour; Derya Kandaz; Muhammed Kursad Ucar; Kemal Polat; Adi Alhudhaif
Journal:  Comput Math Methods Med       Date:  2022-07-19       Impact factor: 2.809

3.  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

4.  Novel Polydimethylsiloxane (PDMS) Pulsatile Vascular Tissue Phantoms for the In-Vitro Investigation of Light Tissue Interaction in Photoplethysmography.

Authors:  Michelle Nomoni; James M May; Panayiotis A Kyriacou
Journal:  Sensors (Basel)       Date:  2020-07-30       Impact factor: 3.576

  4 in total

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