Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.12188/8900| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Simjanoska, Monika | en_US |
| dc.contributor.author | Gjoreski, Martin | en_US |
| dc.contributor.author | Gams, Matjaž | en_US |
| dc.contributor.author | Madevska Bogdanova, Ana | en_US |
| dc.date.accessioned | 2020-09-05T15:03:27Z | - |
| dc.date.available | 2020-09-05T15:03:27Z | - |
| dc.date.issued | 2018-04-11 | - |
| dc.identifier.uri | http://hdl.handle.net/20.500.12188/8900 | - |
| dc.description.abstract | Blood pressure (BP) measurements have been used widely in clinical and private environments. Recently, the use of ECG monitors has proliferated; however, they are not enabled with BP estimation. We have developed a method for BP estimation using only electrocardiogram (ECG) signals. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | MDPI AG | en_US |
| dc.relation.ispartof | Sensors (Basel, Switzerland) | en_US |
| dc.title | Non-Invasive Blood Pressure Estimation from ECG Using Machine Learning Techniques | en_US |
| dc.type | Article | en_US |
| dc.identifier.doi | 10.3390/s18041160 | - |
| dc.identifier.url | http://www.mdpi.com/1424-8220/18/4/1160/pdf | - |
| dc.identifier.volume | 18 | - |
| dc.identifier.issue | 4 | - |
| item.grantfulltext | open | - |
| item.fulltext | With Fulltext | - |
| 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: Journal Articles | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| sensors-18-01160 (1).pdf | 1.11 MB | Adobe PDF | View/Open |
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