Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.12188/24995
DC Field | Value | Language |
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dc.contributor.author | Kuzmanov, Ivan | en_US |
dc.contributor.author | Vasilevska, Anastasija | en_US |
dc.contributor.author | Madevska Bogdanova, Ana | en_US |
dc.contributor.author | Ackovska, Nevena | en_US |
dc.contributor.author | Kostoska, Magdalena | en_US |
dc.contributor.author | Lehocki, Fedor | en_US |
dc.date.accessioned | 2022-12-19T10:39:26Z | - |
dc.date.available | 2022-12-19T10:39:26Z | - |
dc.date.issued | 2022-09 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.12188/24995 | - |
dc.description.abstract | Blood pressure (BP) estimation can aid the triage process and help prioritizing and helping injured, especially in a situation of multiple casualties. The presented research aims to create a model for BP class estimation using electrocardiogram (ECG) and photoplethysmogram (PPG) waveforms. We focus on developing a BP classification model as a convolutional neural network (CNN) - gated recurrent unit (LSTM) hybrid model, containing both CNN and LSTM layers. The used dataset is the publicly available UCI Machine Learning Repository dataset. We have achieved stable AUCROC for each class - 0.89, 0.83, and 0.89 respectively and overall accuracy of 83%. | en_US |
dc.subject | electrocardiogram · photoplethysmogram · blood pressure estimation · triage · LSTM · artificial neural network · deep learning · CNN-LSTM hybrid model | en_US |
dc.title | Blood pressure classification using CNN-LSTM model | en_US |
dc.type | Proceedings | en_US |
dc.relation.conference | ICT Innovations 2022 | en_US |
item.fulltext | With Fulltext | - |
item.grantfulltext | open | - |
crisitem.author.dept | Faculty of Computer Science and Engineering | - |
crisitem.author.dept | Faculty of Computer Science and Engineering | - |
Appears in Collections: | Faculty of Computer Science and Engineering: Conference papers |
Files in This Item:
File | Description | Size | Format | |
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blood-pressure-classification-using-cnn-lstm-model.pdf | 388.97 kB | Adobe PDF | View/Open |
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