Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.12188/24997
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Andrashikova, Barbora | en_US |
dc.contributor.author | Lehocki, Fedor | en_US |
dc.contributor.author | Tyshler, Milan | en_US |
dc.contributor.author | Madevska Bogdanova, Ana | en_US |
dc.contributor.author | Kuzmanov, Ivan | en_US |
dc.contributor.author | Masar, Oto | en_US |
dc.contributor.author | Spasenovich, Marko | en_US |
dc.contributor.author | Putekova, Silvia | en_US |
dc.date.accessioned | 2022-12-19T10:46:01Z | - |
dc.date.available | 2022-12-19T10:46:01Z | - |
dc.date.issued | 2022-09 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.12188/24997 | - |
dc.description.abstract | Blood pressure is a crucial vital sign used as an indicator of patient’s medical state. However, the standard methods of measuring blood pressure continuously are not convenient enough in order to be used versatilely. Critical and life threatening situations such as civil disasters require measuring blood pressure as fast and as accurately as possible without the need of manual calibration. In this paper, we introduce several existing blood pressure estimation techniques using machine learning and deep learning algorithms based on ECG and/or PPG signals acquired from a wearable sensor. | en_US |
dc.subject | cuffless blood pressure · ECG · PPG · machine learning · deep learning | en_US |
dc.title | Using Cuffless Non-Invasive Methods for Blood Pressure Estimation: Description of the Selected Solutions | en_US |
dc.type | Proceedings | en_US |
dc.relation.conference | ICT Innovations 2022 | en_US |
item.fulltext | With Fulltext | - |
item.grantfulltext | open | - |
Appears in Collections: | Faculty of Computer Science and Engineering: Conference papers |
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File | Description | Size | Format | |
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using-cuffless-non-invasive-methods-for-blood-pressure-estimation-description-of-the-selected-solutions.pdf | 196.02 kB | Adobe PDF | View/Open |
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