Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12188/24997
Title: Using Cuffless Non-Invasive Methods for Blood Pressure Estimation: Description of the Selected Solutions
Authors: Andrashikova, Barbora
Lehocki, Fedor
Tyshler, Milan
Madevska Bogdanova, Ana
Kuzmanov, Ivan
Masar, Oto
Spasenovich, Marko
Putekova, Silvia
Keywords: cuffless blood pressure · ECG · PPG · machine learning · deep learning
Issue Date: Sep-2022
Conference: ICT Innovations 2022
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.
URI: http://hdl.handle.net/20.500.12188/24997
Appears in Collections:Faculty of Computer Science and Engineering: Conference papers

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