Evidence map›Paper›PMID 33577456›Full record

ArticleIEEE transactions on biomedical circuits and systems2021

Pulse Wave Modeling Using Bio-Impedance Simulation Platform Based on a 3D Time-Varying Circuit Model.

Bassem Ibrahim, Drew A Hall, Roozbeh Jafari

Abstract read
In one paragraph

Article in IEEE transactions on biomedical circuits and systems, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Review
  2. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Bassem Ibrahim
Drew A Hall
Roozbeh Jafari

Funding

An Unobtrusive Continuous Cuff-less Blood Pressure Monitor for Nocturnal HypertensionR01HL151240 · NHLBI · TEXAS ENGINEERING EXPERIMENT STATION · PI JAFARI, ROOZBEH · 2020 to 2024
$3.5M
SCH: INT: A Context-aware Cuff-less Wearable Ambulatory Blood Pressure Monitor using a Bio-Impedance Sensor ArrayR01EB028106 · NIBIB · TEXAS ENGINEERING EXPERIMENT STATION · PI JAFARI, ROOZBEH · 2018 to 2021
$1.2M
NHLBI NIH HHS R01 HL151240NIBIB NIH HHS R01 EB028106
6 · The paper itself

Abstract

Cardiovascular disease (CVD) threatens the lives of many and affects their productivity. Wearable sensors can enable continuous monitoring of hemodynamic parameters to improve the diagnosis and management of CVD. Bio-Impedance (Bio-Z) is an effective non-invasive sensor for arterial pulse wave monitoring based on blood volume changes in the artery due to the deep penetration of its current signal inside the tissue. However, the measured data are significantly affected by the placement of electrodes relative to the artery and the electrode configuration. In this work, we created a Bio-Z simulation platform that models the tissue, arterial pulse wave, and Bio-Z sensing configuration using a 3D circuit model based on a time-varying impedance grid. A new method is proposed to accurately simulate the different tissue types such as blood, fat, muscles, and bones in a 3D circuit model in addition to the pulsatile activity of the arteries through a variable impedance model. This circuit model is simulated in SPICE and can be used to guide design decisions (i.e. electrode placement relative to the artery and electrode configuration) to optimize the monitoring of pulse wave prior to experimentation. We present extensive simulations of the arterial pulse waveform for different sensor locations, electrode sizes, current injection frequencies, and artery depths. These simulations are validated by experimental Bio-Z measurements.

Indexed as

PulsePulse Wave AnalysisElectric ImpedanceElectrodesHeart Rate

Identifiers

PMID33577456
PMCPMC8054996

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.