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UnoViS: the MedIT public unobtrusive vital signs database
While PhysioNet is a large database for standard clinical vital signs measurements, such a database does not exist for unobtrusively measured signals. This inhibits progress in the vital area of signal processing for unobtrusive medical monitoring as not everybody owns the specific measurement syste...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4450479/ https://www.ncbi.nlm.nih.gov/pubmed/26038690 http://dx.doi.org/10.1186/s13755-015-0010-1 |
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author | Wartzek, Tobias Czaplik, Michael Antink, Christoph Hoog Eilebrecht, Benjamin Walocha, Rafael Leonhardt, Steffen |
author_facet | Wartzek, Tobias Czaplik, Michael Antink, Christoph Hoog Eilebrecht, Benjamin Walocha, Rafael Leonhardt, Steffen |
author_sort | Wartzek, Tobias |
collection | PubMed |
description | While PhysioNet is a large database for standard clinical vital signs measurements, such a database does not exist for unobtrusively measured signals. This inhibits progress in the vital area of signal processing for unobtrusive medical monitoring as not everybody owns the specific measurement systems to acquire signals. Furthermore, if no common database exists, a comparison between different signal processing approaches is not possible. This gap will be closed by our UnoViS database. It contains different recordings in various scenarios ranging from a clinical study to measurements obtained while driving a car. Currently, 145 records with a total of 16.2 h of measurement data is available, which are provided as MATLAB files or in the PhysioNet WFDB file format. In its initial state, only (multichannel) capacitive ECG and unobtrusive PPG signals are, together with a reference ECG, included. All ECG signals contain annotations by a peak detector and by a medical expert. A dataset from a clinical study contains further clinical annotations. Additionally, supplementary functions are provided, which simplify the usage of the database and thus the development and evaluation of new algorithms. The development of urgently needed methods for very robust parameter extraction or robust signal fusion in view of frequent severe motion artifacts in unobtrusive monitoring is now possible with the database. |
format | Online Article Text |
id | pubmed-4450479 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-44504792015-06-03 UnoViS: the MedIT public unobtrusive vital signs database Wartzek, Tobias Czaplik, Michael Antink, Christoph Hoog Eilebrecht, Benjamin Walocha, Rafael Leonhardt, Steffen Health Inf Sci Syst Database While PhysioNet is a large database for standard clinical vital signs measurements, such a database does not exist for unobtrusively measured signals. This inhibits progress in the vital area of signal processing for unobtrusive medical monitoring as not everybody owns the specific measurement systems to acquire signals. Furthermore, if no common database exists, a comparison between different signal processing approaches is not possible. This gap will be closed by our UnoViS database. It contains different recordings in various scenarios ranging from a clinical study to measurements obtained while driving a car. Currently, 145 records with a total of 16.2 h of measurement data is available, which are provided as MATLAB files or in the PhysioNet WFDB file format. In its initial state, only (multichannel) capacitive ECG and unobtrusive PPG signals are, together with a reference ECG, included. All ECG signals contain annotations by a peak detector and by a medical expert. A dataset from a clinical study contains further clinical annotations. Additionally, supplementary functions are provided, which simplify the usage of the database and thus the development and evaluation of new algorithms. The development of urgently needed methods for very robust parameter extraction or robust signal fusion in view of frequent severe motion artifacts in unobtrusive monitoring is now possible with the database. BioMed Central 2015-06-02 /pmc/articles/PMC4450479/ /pubmed/26038690 http://dx.doi.org/10.1186/s13755-015-0010-1 Text en © Wartzek et al. 2015 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Database Wartzek, Tobias Czaplik, Michael Antink, Christoph Hoog Eilebrecht, Benjamin Walocha, Rafael Leonhardt, Steffen UnoViS: the MedIT public unobtrusive vital signs database |
title | UnoViS: the MedIT public unobtrusive vital signs database |
title_full | UnoViS: the MedIT public unobtrusive vital signs database |
title_fullStr | UnoViS: the MedIT public unobtrusive vital signs database |
title_full_unstemmed | UnoViS: the MedIT public unobtrusive vital signs database |
title_short | UnoViS: the MedIT public unobtrusive vital signs database |
title_sort | unovis: the medit public unobtrusive vital signs database |
topic | Database |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4450479/ https://www.ncbi.nlm.nih.gov/pubmed/26038690 http://dx.doi.org/10.1186/s13755-015-0010-1 |
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