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.

Basic information

Original name

Sleep scoring using artificial neural networks

Authors

RONZHINA, Marina (203 Czech Republic, guarantor), Oto JANOUŠEK (203 Czech Republic), Jana KOLÁŘOVÁ (203 Czech Republic), Marie NOVÁKOVÁ (203 Czech Republic, belonging to the institution), Petr HONZÍK (203 Czech Republic) and Ivo PROVAZNÍK (203 Czech Republic)

Edition

Sleep Medicine Reviews, 2012, 1087-0792

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

30000 3. Medical and Health Sciences

Country of publisher

United Kingdom of Great Britain and Northern Ireland

Confidentiality degree

není předmětem státního či obchodního tajemství

Impact factor

Impact factor: 8.681

RIV identification code

RIV/00216224:14110/12:00057213

Organization unit

Faculty of Medicine

DOI

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

UT WoS

000303429000007

Keywords in English

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

Abstract

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.

Links

GD102/09/H083, research and development project

Name: Informační technologie v biomedicínském inženýrství

Investor: Czech Science Foundation

MSM0021622402, plan (intention)

Name: Časná diagnostika a léčba kardiovaskulárních chorob

Investor: Ministry of Education, Youth and Sports of the CR, Early diagnostics and treatment of cardiovascular diseases

Citovat

RONZHINA, Marina, Oto JANOUŠEK, Jana KOLÁŘOVÁ, Marie NOVÁKOVÁ, Petr HONZÍK and Ivo PROVAZNÍK. Sleep scoring using artificial neural networks. Sleep Medicine Reviews. 2012, vol. 16, No 3, p. 251-263. ISSN 1087-0792. Available from: 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 and Ivo PROVAZNÍK. Sleep scoring using artificial neural networks. \textit{Sleep Medicine Reviews}. 2012, vol.~16, No~3, p.~251-263. ISSN~1087-0792. Available from: https://dx.doi.org/10.1016/j.smrv.2011.06.003.

Detailed Information on Publication Record