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Tytuł:
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Continuous Non-Invasive Blood Pressure Measurement Using 60 GHz-Radar-A Feasibility Study.
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Autorzy:
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Vysotskaya N; Infineon Technologies AG, Am Campeon 1-15, 85579 Neubiberg, Germany.; Department for Computer Science 5 (Pattern Recognition), Friedrich-Alexander-University Erlangen-Nuremberg (FAU), Martensstrasse 3, 91058 Erlangen, Germany.
Will C; Infineon Technologies AG, Am Campeon 1-15, 85579 Neubiberg, Germany.
Servadei L; Department of Electrical and Computer Engineering, Technical University of Munich, Arcisstrasse 21, 80333 Munich, Germany.
Maul N; Department for Computer Science 5 (Pattern Recognition), Friedrich-Alexander-University Erlangen-Nuremberg (FAU), Martensstrasse 3, 91058 Erlangen, Germany.
Mandl C; Infineon Technologies AG, Am Campeon 1-15, 85579 Neubiberg, Germany.
Nau M; Department for Computer Science 5 (Pattern Recognition), Friedrich-Alexander-University Erlangen-Nuremberg (FAU), Martensstrasse 3, 91058 Erlangen, Germany.
Harnisch J; Infineon Technologies AG, Am Campeon 1-15, 85579 Neubiberg, Germany.
Maier A; Department for Computer Science 5 (Pattern Recognition), Friedrich-Alexander-University Erlangen-Nuremberg (FAU), Martensstrasse 3, 91058 Erlangen, Germany.
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Źródło:
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Sensors (Basel, Switzerland) [Sensors (Basel)] 2023 Apr 19; Vol. 23 (8). Date of Electronic Publication: 2023 Apr 19.
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Typ publikacji:
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Journal Article
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Język:
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English
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Imprint Name(s):
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Original Publication: Basel, Switzerland : MDPI, c2000-
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MeSH Terms:
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Radar*
Blood Pressure Determination*
Humans ; Blood Pressure/physiology ; Feasibility Studies ; Sphygmomanometers
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References:
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Grant Information:
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16KISK218 Federal Ministry of Education and Research
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Contributed Indexing:
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Keywords: FMCW radar; continuous blood pressure monitoring; signal processing; vital sensing; wearable device
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Entry Date(s):
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Date Created: 20230428 Date Completed: 20230501 Latest Revision: 20230501
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Update Code:
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20240105
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PubMed Central ID:
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PMC10145629
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DOI:
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10.3390/s23084111
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PMID:
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37112454
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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.
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