Sleep Apnea Detection Based on Dynamic Neural Networks

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)

Abstract

One of widespread breath disruption that takes place during sleep is apnea, during this anomaly people are not able to get enough oxygen. The article describes method for breathing analyses that is based on neural network that allows recognition of breath patterns and predicting anomalies that may occur. Class of machine learning algorithms includes lots of models, widespread feed forward networks are able to solve task of classification, but are not quite suitable for processing time-series data. The paper describes results of teaching and testing several types of dynamic or recurrent networks: NARX, Elman, distributed and focused time delay.

Original languageEnglish
Title of host publicationCommunications in Computer and Information Science
PublisherSpringer Verlag
Pages556-567
Number of pages12
Volume466 CCIS
ISBN (Print)9783319118536
DOIs
Publication statusPublished - 2014
Event11th Joint Conference on Knowledge-Based Software Engineering, JCKBSE 2014 - Volgograd, Russian Federation
Duration: 17 Sep 201420 Sep 2014

Publication series

NameCommunications in Computer and Information Science
Volume466 CCIS
ISSN (Print)18650929

Other

Other11th Joint Conference on Knowledge-Based Software Engineering, JCKBSE 2014
CountryRussian Federation
CityVolgograd
Period17.9.1420.9.14

Fingerprint

Learning algorithms
Learning systems
Time series
Time delay
Teaching
Neural networks
Oxygen
Testing
Processing
Sleep

Keywords

  • breath pattern
  • recurrent neural network
  • Sleep apnea

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Devjatykh, D., Gerget, O. M., & Berestneva, O. G. (2014). Sleep Apnea Detection Based on Dynamic Neural Networks. In Communications in Computer and Information Science (Vol. 466 CCIS, pp. 556-567). (Communications in Computer and Information Science; Vol. 466 CCIS). Springer Verlag. https://doi.org/10.1007/978-3-319-11854-3_48

Sleep Apnea Detection Based on Dynamic Neural Networks. / Devjatykh, Dmitry; Gerget, Olga Mikhaylovna; Berestneva, Olga G.

Communications in Computer and Information Science. Vol. 466 CCIS Springer Verlag, 2014. p. 556-567 (Communications in Computer and Information Science; Vol. 466 CCIS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Devjatykh, D, Gerget, OM & Berestneva, OG 2014, Sleep Apnea Detection Based on Dynamic Neural Networks. in Communications in Computer and Information Science. vol. 466 CCIS, Communications in Computer and Information Science, vol. 466 CCIS, Springer Verlag, pp. 556-567, 11th Joint Conference on Knowledge-Based Software Engineering, JCKBSE 2014, Volgograd, Russian Federation, 17.9.14. https://doi.org/10.1007/978-3-319-11854-3_48
Devjatykh D, Gerget OM, Berestneva OG. Sleep Apnea Detection Based on Dynamic Neural Networks. In Communications in Computer and Information Science. Vol. 466 CCIS. Springer Verlag. 2014. p. 556-567. (Communications in Computer and Information Science). https://doi.org/10.1007/978-3-319-11854-3_48
Devjatykh, Dmitry ; Gerget, Olga Mikhaylovna ; Berestneva, Olga G. / Sleep Apnea Detection Based on Dynamic Neural Networks. Communications in Computer and Information Science. Vol. 466 CCIS Springer Verlag, 2014. pp. 556-567 (Communications in Computer and Information Science).
@inproceedings{fd0a051d502549b58a87a7f958bee61d,
title = "Sleep Apnea Detection Based on Dynamic Neural Networks",
abstract = "One of widespread breath disruption that takes place during sleep is apnea, during this anomaly people are not able to get enough oxygen. The article describes method for breathing analyses that is based on neural network that allows recognition of breath patterns and predicting anomalies that may occur. Class of machine learning algorithms includes lots of models, widespread feed forward networks are able to solve task of classification, but are not quite suitable for processing time-series data. The paper describes results of teaching and testing several types of dynamic or recurrent networks: NARX, Elman, distributed and focused time delay.",
keywords = "breath pattern, recurrent neural network, Sleep apnea",
author = "Dmitry Devjatykh and Gerget, {Olga Mikhaylovna} and Berestneva, {Olga G.}",
year = "2014",
doi = "10.1007/978-3-319-11854-3_48",
language = "English",
isbn = "9783319118536",
volume = "466 CCIS",
series = "Communications in Computer and Information Science",
publisher = "Springer Verlag",
pages = "556--567",
booktitle = "Communications in Computer and Information Science",

}

TY - GEN

T1 - Sleep Apnea Detection Based on Dynamic Neural Networks

AU - Devjatykh, Dmitry

AU - Gerget, Olga Mikhaylovna

AU - Berestneva, Olga G.

PY - 2014

Y1 - 2014

N2 - One of widespread breath disruption that takes place during sleep is apnea, during this anomaly people are not able to get enough oxygen. The article describes method for breathing analyses that is based on neural network that allows recognition of breath patterns and predicting anomalies that may occur. Class of machine learning algorithms includes lots of models, widespread feed forward networks are able to solve task of classification, but are not quite suitable for processing time-series data. The paper describes results of teaching and testing several types of dynamic or recurrent networks: NARX, Elman, distributed and focused time delay.

AB - One of widespread breath disruption that takes place during sleep is apnea, during this anomaly people are not able to get enough oxygen. The article describes method for breathing analyses that is based on neural network that allows recognition of breath patterns and predicting anomalies that may occur. Class of machine learning algorithms includes lots of models, widespread feed forward networks are able to solve task of classification, but are not quite suitable for processing time-series data. The paper describes results of teaching and testing several types of dynamic or recurrent networks: NARX, Elman, distributed and focused time delay.

KW - breath pattern

KW - recurrent neural network

KW - Sleep apnea

UR - http://www.scopus.com/inward/record.url?scp=84907339520&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84907339520&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-11854-3_48

DO - 10.1007/978-3-319-11854-3_48

M3 - Conference contribution

AN - SCOPUS:84907339520

SN - 9783319118536

VL - 466 CCIS

T3 - Communications in Computer and Information Science

SP - 556

EP - 567

BT - Communications in Computer and Information Science

PB - Springer Verlag

ER -