| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0490307 |
| utime |
20240103220122.1 |
| mtime |
20180614235959.9 |
| title
(primary) (eng) |
Dynamic Bayesian Networks for the Classification of Sleep Stages |
| specification |
| page_count |
11 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0490306 |
| ISBN |
978-80-7378-361-7 |
| title
|
Proceedings of the 11th Workshop on Uncertainty Processing (WUPES’18) |
| page_num |
205-215 |
| publisher |
| place |
Praha |
| name |
MatfyzPress, Publishing House of the Faculty of Mathematics and Physics Charles University |
| year |
2018 |
|
| editor |
| name1 |
Kratochvíl |
| name2 |
Václav |
|
| editor |
| name1 |
Vejnarová |
| name2 |
Jiřina |
|
|
| keyword |
Dynamic Bayesian Network |
| keyword |
Sleep Analysis |
| author
(primary) |
| ARLID |
cav_un_auth*0101228 |
| name1 |
Vomlel |
| name2 |
Jiří |
| full_dept (cz) |
Matematická teorie rozhodování |
| full_dept (eng) |
Department of Decision Making Theory |
| department (cz) |
MTR |
| department (eng) |
MTR |
| institution |
UTIA-B |
| full_dept |
Department of Decision Making Theory |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0216188 |
| name1 |
Kratochvíl |
| name2 |
Václav |
| full_dept (cz) |
Matematická teorie rozhodování |
| full_dept |
Department of Decision Making Theory |
| department (cz) |
MTR |
| department |
MTR |
| institution |
UTIA-B |
| full_dept |
Department of Decision Making Theory |
| country |
CZ |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| project_id |
GA16-12010S |
| agency |
GA ČR |
| country |
CZ |
| ARLID |
cav_un_auth*0332303 |
|
| project |
| project_id |
GA17-08182S |
| agency |
GA ČR |
| ARLID |
cav_un_auth*0348851 |
|
| abstract
(eng) |
Human sleep is traditionally classified into five (or six) stages. The manual classification is time consuming since it requires knowledge of an extensive set of rules from manuals and experienced experts. Therefore automatic classification methods appear useful for this task. In this paper we extend the approach based on Hidden Markov Models by relating certain features not only to the current time slice but also to the previous one. Dynamic Bayesian Networks that results from this generalization are thus capable of modeling features related to state transitions. Experiments on real data revealed that in this way we are able to increase the prediction accuracy. |
| action |
| ARLID |
cav_un_auth*0361637 |
| name |
Workshop on Uncertainty Processing (WUPES’18) |
| dates |
20180606 |
| place |
Třeboň |
| country |
CZ |
| mrcbC20-s |
20180609 |
|
| RIV |
JD |
| FORD0 |
10000 |
| FORD1 |
10200 |
| FORD2 |
10201 |
| reportyear |
2019 |
| num_of_auth |
2 |
| presentation_type |
PR |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0284594 |
| mrcbC62 |
1 |
| confidential |
S |
| arlyear |
2018 |
| mrcbU14 |
SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
WOS |
| mrcbU63 |
cav_un_epca*0490306 Proceedings of the 11th Workshop on Uncertainty Processing (WUPES’18) MatfyzPress, Publishing House of the Faculty of Mathematics and Physics Charles University 2018 Praha 205 215 978-80-7378-361-7 |
| mrcbU67 |
340 Kratochvíl Václav |
| mrcbU67 |
340 Vejnarová Jiřina |
|