Publication result detail

Sleep Scoring using Artificial Neural Networks

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

Original Title

Sleep Scoring using Artificial Neural Networks

English Title

Sleep Scoring using Artificial Neural Networks

Type

Peer-reviewed article not indexed in WoS or Scopus

Original Abstract

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.

English abstract

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.

Keywords

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

Key words in English

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

Authors

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

RIV year

2016

Released

01.06.2012

Publisher

Elsevier

ISBN

1087-0792

Periodical

SLEEP MEDICINE REVIEWS

Volume

2012

Number

16

State

United Kingdom of Great Britain and Northern Ireland

Pages from

251

Pages to

263

Pages count

13

BibTex

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