Detail publikace

Sleep Scoring using Artificial Neural Networks

RONZHINA, M. JANOUŠEK, O. KOLÁŘOVÁ, J. NOVÁKOVÁ, M. HONZÍK, P. PROVAZNÍK, I.

Originální název

Sleep Scoring using Artificial Neural Networks

Typ

článek v časopise - ostatní, Jost

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

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

Autoři

RONZHINA, M.; JANOUŠEK, O.; KOLÁŘOVÁ, J.; NOVÁKOVÁ, M.; HONZÍK, P.; PROVAZNÍK, I.

Rok RIV

2012

Vydáno

1. 6. 2012

Nakladatel

Elsevier

ISSN

1087-0792

Periodikum

SLEEP MEDICINE REVIEWS

Ročník

2012

Číslo

16

Stát

Spojené království Velké Británie a Severního Irska

Strany od

251

Strany do

263

Strany počet

13

BibTex

@article{BUT73020,
  author="Marina {Filipenská} and Oto {Janoušek} and Jana {Kolářová} and Marie {Nováková} and Petr {Honzík} and Valentine {Provazník}",
  title="Sleep Scoring using Artificial Neural Networks",
  journal="SLEEP MEDICINE REVIEWS",
  year="2012",
  volume="2012",
  number="16",
  pages="251--263",
  issn="1087-0792"
}