Publication detail

PSG-based classification of sleep phases

KRÁLÍK, M. RONZHINA, M.

Original Title

PSG-based classification of sleep phases

Czech Title

PSG-based classification of sleep phases

English Title

PSG-based classification of sleep phases

Type

conference paper

Language

cs

Original Abstract

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.

Czech abstract

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.

English abstract

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.

Keywords

Polysomnography, sleep scoring, classification features, neural networks

RIV year

2015

Released

03.09.2015

Publisher

VUT v Brně

Location

Brno, Česká republika

ISBN

978-80-214-5148-3

Book

Proceedings of the 21th Student Competition Conference

Edition number

první

Pages from

215

Pages to

217

Pages count

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"
}