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.

Original language	English
Title of host publication	Communications in Computer and Information Science
Publisher	Springer Verlag
Pages	556-567
Number of pages	12
Volume	466 CCIS
ISBN (Print)	9783319118536
DOIs	https://doi.org/10.1007/978-3-319-11854-3_48
Publication status	Published - 2014
Event	11th Joint Conference on Knowledge-Based Software Engineering, JCKBSE 2014 - Volgograd, Russian Federation Duration: 17 Sep 2014 → 20 Sep 2014

Publication series

Name	Communications in Computer and Information Science
Volume	466 CCIS
ISSN (Print)	18650929

Other

Other	11th Joint Conference on Knowledge-Based Software Engineering, JCKBSE 2014
Country	Russian Federation
City	Volgograd
Period	17.9.14 → 20.9.14

Keywords

breath pattern
recurrent neural network
Sleep apnea

ASJC Scopus subject areas

Computer Science(all)

Access to Document

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

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Cite this

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 proceeding › Conference 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

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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.

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