Sleep Apnea Detection Based on Dynamic Neural Networks

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10 Цитирования (Scopus)

Аннотация

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.

Язык оригиналаАнглийский
Название основной публикацииCommunications in Computer and Information Science
ИздательSpringer Verlag
Страницы556-567
Число страниц12
Том466 CCIS
ISBN (печатное издание)9783319118536
DOI
СостояниеОпубликовано - 2014
Событие11th Joint Conference on Knowledge-Based Software Engineering, JCKBSE 2014 - Volgograd, Российская Федерация
Продолжительность: 17 сен 201420 сен 2014

Серия публикаций

НазваниеCommunications in Computer and Information Science
Том466 CCIS
ISSN (печатное издание)18650929

Другое

Другое11th Joint Conference on Knowledge-Based Software Engineering, JCKBSE 2014
СтранаРоссийская Федерация
ГородVolgograd
Период17.9.1420.9.14

ASJC Scopus subject areas

  • Computer Science(all)

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