Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12188/24997
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dc.contributor.authorAndrashikova, Barboraen_US
dc.contributor.authorLehocki, Fedoren_US
dc.contributor.authorTyshler, Milanen_US
dc.contributor.authorMadevska Bogdanova, Anaen_US
dc.contributor.authorKuzmanov, Ivanen_US
dc.contributor.authorMasar, Otoen_US
dc.contributor.authorSpasenovich, Markoen_US
dc.contributor.authorPutekova, Silviaen_US
dc.date.accessioned2022-12-19T10:46:01Z-
dc.date.available2022-12-19T10:46:01Z-
dc.date.issued2022-09-
dc.identifier.urihttp://hdl.handle.net/20.500.12188/24997-
dc.description.abstractBlood 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.subjectcuffless blood pressure · ECG · PPG · machine learning · deep learningen_US
dc.titleUsing Cuffless Non-Invasive Methods for Blood Pressure Estimation: Description of the Selected Solutionsen_US
dc.typeProceedingsen_US
dc.relation.conferenceICT Innovations 2022en_US
item.fulltextWith Fulltext-
item.grantfulltextopen-
Appears in Collections:Faculty of Computer Science and Engineering: Conference papers
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