Sleep scoring using artificial neural networks

J 2012

Sleep scoring using artificial neural networks

RONZHINA, Marina, Oto JANOUŠEK, Jana KOLÁŘOVÁ, Marie NOVÁKOVÁ, Petr HONZÍK et. al.

Základní údaje

Originální název

Sleep scoring using artificial neural networks

Autoři

RONZHINA, Marina (203 Česká republika, garant), Oto JANOUŠEK (203 Česká republika), Jana KOLÁŘOVÁ (203 Česká republika), Marie NOVÁKOVÁ (203 Česká republika, domácí), Petr HONZÍK (203 Česká republika) a Ivo PROVAZNÍK (203 Česká republika)

Vydání

Sleep Medicine Reviews, 2012, 1087-0792

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

30000 3. Medical and Health Sciences

Stát vydavatele

Velká Británie a Severní Irsko

Utajení

není předmětem státního či obchodního tajemství

Impakt faktor

Impact factor: 8.681

Kód RIV

RIV/00216224:14110/12:00057213

Organizační jednotka

Lékařská fakulta

DOI

http://dx.doi.org/10.1016/j.smrv.2011.06.003

UT WoS

000303429000007

Klíčová slova anglicky

Polysomnographic data; Sleep scoring; Features extraction; Artificial neural networks

Příznaky

Mezinárodní význam

Změněno: 22. 4. 2013 13:42, Soňa Böhmová

Anotace

V originále

Rapid development of computer technologies leads to the intensive automation of many different processes traditionally performed by human experts. One of the spheres characterized by the introduction of new high intelligence technologies substituting analysis performed by humans is sleep scoring. This refers to the classification task and can be solved e next to other classification methods e by use of artificial neural networks (ANN). ANNs are parallel adaptive systems suitable for solving of nonlinear problems. Using ANN for automatic sleep scoring is especially promising because of new ANN learning algorithms allowing faster classification without decreasing the performance. Both appropriate preparation of training data as well as selection of the ANN model make it possible to perform effective and correct recognizing of relevant sleep stages. Such an approach is highly topical, taking into consideration the fact that there is no automatic scorer utilizing ANN technology available at present.

Návaznosti

GD102/09/H083, projekt VaV

Název: Informační technologie v biomedicínském inženýrství

Investor: Grantová agentura ČR, Informační technologie v biomedicínckém inženýrství

MSM0021622402, záměr

Název: Časná diagnostika a léčba kardiovaskulárních chorob

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, Časná diagnostika a léčba kardiovaskulárních chorob

Citovat

RONZHINA, Marina, Oto JANOUŠEK, Jana KOLÁŘOVÁ, Marie NOVÁKOVÁ, Petr HONZÍK a Ivo PROVAZNÍK. Sleep scoring using artificial neural networks. Sleep Medicine Reviews. 2012, roč. 16, č. 3, s. 251-263. ISSN 1087-0792. Dostupné z: https://dx.doi.org/10.1016/j.smrv.2011.06.003.

@article{959003,
   author = {Ronzhina, Marina and Janoušek, Oto and Kolářová, Jana and Nováková, Marie and Honzík, Petr and Provazník, Ivo},
   article_number = {3},
   doi = {http://dx.doi.org/10.1016/j.smrv.2011.06.003},
   keywords = {Polysomnographic data; Sleep scoring; Features extraction; Artificial neural networks},
   language = {eng},
   issn = {1087-0792},
   journal = {Sleep Medicine Reviews},
   title = {Sleep scoring using artificial neural networks},
   volume = {16},
   year = {2012}
}

TY  - JOUR
ID  - 959003
AU  - Ronzhina, Marina - Janoušek, Oto - Kolářová, Jana - Nováková, Marie - Honzík, Petr - Provazník, Ivo
PY  - 2012
TI  - Sleep scoring using artificial neural networks
JF  - Sleep Medicine Reviews
VL  - 16
IS  - 3
SP  - 251-263
EP  - 251-263
SN  - 10870792
KW  - Polysomnographic data
KW  - Sleep scoring
KW  - Features extraction
KW  - Artificial neural networks
N2  - Rapid development of computer technologies leads to the intensive automation of many different processes traditionally performed by human experts. One of the spheres characterized by the introduction of new high intelligence technologies substituting analysis performed by humans is sleep scoring. This refers to the classification task and can be solved e next to other classification methods e by use of artificial neural networks (ANN). ANNs are parallel adaptive systems suitable for solving of nonlinear problems. Using ANN for automatic sleep scoring is especially promising because of new ANN learning algorithms allowing faster classification without decreasing the performance. Both appropriate preparation of training data as well as selection of the ANN model make it possible to perform effective and correct recognizing of relevant sleep stages. Such an approach is highly topical, taking into consideration the fact that there is no automatic scorer utilizing ANN technology available at present.
ER  -

RONZHINA, Marina, Oto JANOUŠEK, Jana KOLÁŘOVÁ, Marie NOVÁKOVÁ, Petr HONZÍK a Ivo PROVAZNÍK. Sleep scoring using artificial neural networks. \textit{Sleep Medicine Reviews}. 2012, roč.~16, č.~3, s.~251-263. ISSN~1087-0792. Dostupné z: https://dx.doi.org/10.1016/j.smrv.2011.06.003.

Podrobný výpis o publikaci