Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12188/30467
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dc.contributor.authorKostoska, Magdalenaen_US
dc.contributor.authorKuzmanov, Ivanen_US
dc.contributor.authorKoteska, Bojanaen_US
dc.contributor.authorLehocki, Fedoren_US
dc.contributor.authorMadevska Bogdanova, Anaen_US
dc.date.accessioned2024-06-07T07:45:24Z-
dc.date.available2024-06-07T07:45:24Z-
dc.date.issued2023-10-
dc.identifier.urihttp://hdl.handle.net/20.500.12188/30467-
dc.description.abstractThe development of non-invasive easily available blood pressure estimation methods using electrocardiogram - ECG and/or photoplethysmogram - PPG signals has gained increasing attention. However, there is a lack of consistency in the evaluation of these methods due to variations in the size and availability of data in published datasets. Our research involves retrieving, cleaning, and storing a portion of the MIMIC-III database for utilization in model training and testing. This paper outlines our methodology for processing the MIMIC-III database, along with the challenges encountered during the process.en_US
dc.subjectMIMIC-III · electrocardiogram · photoplethysmogram · blood pressure estimation · artificial neural network · deep learning.en_US
dc.titleProcessing MIMIC-III for Evaluation of Various Blood Pressure Estimation Modelsen_US
dc.typeProceedingsen_US
dc.relation.conferenceICT Innovations, 24-26 September, 2023, Ohrid, N. Macedoniaen_US
item.grantfulltextopen-
item.fulltextWith Fulltext-
crisitem.author.deptFaculty of Computer Science and Engineering-
crisitem.author.deptFaculty of Computer Science and Engineering-
Appears in Collections:Faculty of Computer Science and Engineering: Conference papers
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