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

Keywords

  • breath pattern
  • recurrent neural network
  • Sleep apnea

ASJC Scopus subject areas

  • Computer Science(all)

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