Evidence map›Paper›PMID 37112454›Full record

ArticleSensors (Basel, Switzerland)2023

Continuous Non-Invasive Blood Pressure Measurement Using 60 GHz-Radar-A Feasibility Study.

Nastassia Vysotskaya, Christoph Will, Lorenzo Servadei, Noah Maul, Christian Mandl, Merlin Nau, Jens Harnisch, Andreas Maier

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
3.4field-weighted citation impact, top 7% of its field
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

10 citing papers in PubMed, 31 citations in OpenAlex.

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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

8 authors at 3 institutions in 1 country.

Nastassia VysotskayaInfineon Technologies AG, Am Campeon 1-15, 85579 Neubiberg, Germany.ORCID 0000-0003-1367-7654
Christoph WillInfineon Technologies AG, Am Campeon 1-15, 85579 Neubiberg, Germany.ORCID 0000-0002-1492-1551
Lorenzo ServadeiDepartment of Electrical and Computer Engineering, Technical University of Munich, Arcisstrasse 21, 80333 Munich, Germany.ORCID 0000-0003-4322-834X
Noah MaulDepartment for Computer Science 5 (Pattern Recognition), Friedrich-Alexander-University Erlangen-Nuremberg (FAU), Martensstrasse 3, 91058 Erlangen, Germany.ORCID 0000-0003-1161-1299
Christian MandlInfineon Technologies AG, Am Campeon 1-15, 85579 Neubiberg, Germany.
Merlin NauDepartment for Computer Science 5 (Pattern Recognition), Friedrich-Alexander-University Erlangen-Nuremberg (FAU), Martensstrasse 3, 91058 Erlangen, Germany.ORCID 0009-0006-6468-0270
Jens HarnischInfineon Technologies AG, Am Campeon 1-15, 85579 Neubiberg, Germany.
Andreas MaierDepartment for Computer Science 5 (Pattern Recognition), Friedrich-Alexander-University Erlangen-Nuremberg (FAU), Martensstrasse 3, 91058 Erlangen, Germany.ORCID 0000-0002-9550-5284
Infineon Technologies (Germany) · DEFriedrich-Alexander-Universität Erlangen-Nürnberg · DETechnical University of Munich · DE

Funding

Federal Ministry of Education and Research 16KISK218
6 · The paper itself

Abstract

Blood pressure monitoring is of paramount importance in the assessment of a human's cardiovascular health. The state-of-the-art method remains the usage of an upper-arm cuff sphygmomanometer. However, this device suffers from severe limitations-it only provides a static blood pressure value pair, is incapable of capturing blood pressure variations over time, is inaccurate, and causes discomfort upon use. This work presents a radar-based approach that utilizes the movement of the skin due to artery pulsation to extract pressure waves. From those waves, a set of 21 features was collected and used-together with the calibration parameters of age, gender, height, and weight-as input for a neural network-based regression model. After collecting data from 55 subjects from radar and a blood pressure reference device, we trained 126 networks to analyze the developed approach's predictive power. As a result, a very shallow network with just two hidden layers produced a systolic error of 9.2±8.3 mmHg (mean error ± standard deviation) and a diastolic error of 7.7±5.7 mmHg. While the trained model did not reach the requirements of the AAMI and BHS blood pressure measuring standards, optimizing network performance was not the goal of the proposed work. Still, the approach has displayed great potential in capturing blood pressure variation with the proposed features. The presented approach therefore shows great potential to be incorporated into wearable devices for continuous blood pressure monitoring for home use or screening applications, after improving this approach even further.

Indexed as

Blood Pressure DeterminationRadarBlood PressureFeasibility StudiesHumansSphygmomanometerscontinuous blood pressure monitoringFMCW radarsignal processingvital sensingwearable device

Identifiers

PMID37112454
PMCPMC10145629
OpenAlexW4366580981

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.