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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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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