Detail publikace

PSG-based classification of sleep phases

KRÁLÍK, M. RONZHINA, M.

Originální název

PSG-based classification of sleep phases

Český název

PSG-based classification of sleep phases

Anglický název

PSG-based classification of sleep phases

Typ

článek ve sborníku

Jazyk

cs

Originální abstrakt

This work is focused on classification of sleep phases using artificial neural network. The unconventional approach was used for calculation of classification features using polysomnographic data (PSG) of real patients. This approach allows to increase the time resolution of the analysis and, thus, to achieve more accurate results of classification.

Český abstrakt

This work is focused on classification of sleep phases using artificial neural network. The unconventional approach was used for calculation of classification features using polysomnographic data (PSG) of real patients. This approach allows to increase the time resolution of the analysis and, thus, to achieve more accurate results of classification.

Anglický abstrakt

This work is focused on classification of sleep phases using artificial neural network. The unconventional approach was used for calculation of classification features using polysomnographic data (PSG) of real patients. This approach allows to increase the time resolution of the analysis and, thus, to achieve more accurate results of classification.

Klíčová slova

polysomnography, sleep scoring, classification features, neural networks

Rok RIV

2015

Vydáno

03.09.2015

Nakladatel

VUT v Brně

Místo

Brno, Česká republika

ISBN

978-80-214-5148-3

Kniha

Proceedings of the 21th Student Competition Conference

Číslo edice

první

Strany od

215

Strany do

217

Strany počet

3

URL

BibTex


@inproceedings{BUT115887,
  author="Martin {Králík} and Marina {Ronzhina}",
  title="PSG-based classification of sleep phases",
  annote="This work is focused on classification of sleep phases using artificial neural network. The unconventional approach was used for calculation of classification features using polysomnographic data (PSG) of real patients. This approach allows to increase the time resolution of the analysis and, thus, to achieve more accurate results of classification.",
  address="VUT v Brně",
  booktitle="Proceedings of the 21th Student Competition Conference",
  chapter="115887",
  howpublished="online",
  institution="VUT v Brně",
  year="2015",
  month="september",
  pages="215--217",
  publisher="VUT v Brně",
  type="conference paper"
}