PSG-Based Classification of Sleep Phases
| but.event.date | 23.04.2015 | cs |
| but.event.title | Student EEICT 2015 | cs |
| dc.contributor.author | Králík, M. | |
| dc.date.accessioned | 2015-08-25T08:42:57Z | |
| dc.date.available | 2015-08-25T08:42:57Z | |
| dc.date.issued | 2015 | cs |
| dc.description.abstract | This work is focused on classification of sleep phases using artificial neural network. The unconventional approach was used for calculation of classification features using polysomnographic data (PSG) of real patients. This approach allows to increase the time resolution of the analysis and, thus, to achieve more accurate results of classification. | en |
| dc.format | text | cs |
| dc.format.extent | 215-217 | cs |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Proceedings of the 21st Conference STUDENT EEICT 2015. s. 215-217. ISBN 978-80-214-5148-3 | cs |
| dc.identifier.isbn | 978-80-214-5148-3 | |
| dc.identifier.uri | http://hdl.handle.net/11012/42980 | |
| dc.language.iso | cs | cs |
| dc.publisher | Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií | cs |
| dc.relation.ispartof | Proceedings of the 21st Conference STUDENT EEICT 2015 | en |
| dc.relation.uri | http://www.feec.vutbr.cz/EEICT/ | cs |
| dc.rights | © Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií | cs |
| dc.rights.access | openAccess | en |
| dc.subject | polysomnography | en |
| dc.subject | sleep scoring | en |
| dc.subject | classification features | en |
| dc.subject | neural networks | en |
| dc.title | PSG-Based Classification of Sleep Phases | en |
| dc.type.driver | conferenceObject | en |
| dc.type.status | Peer-reviewed | en |
| dc.type.version | publishedVersion | en |
| eprints.affiliatedInstitution.department | Fakulta elektrotechniky a komunikačních technologií | cs |