Sleep stages classification using shallow classifiers

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Citations (Scopus)

Abstract

A person with sleep disorder such as apnea will stop breathing for a while during sleep. If frequently occurs, sleep disorder is dangerous for health. An early step for diagnosing apnea is by classifying the sleep stages during sleep. This study explores some shallow classifiers and their feasibility applied to sleep data. Recently, a sleep stages classification system that use deep unsupervised features learning representations have been proposed [9]. In our view, an adequate study on this problem using shallow classifiers still need to be investigated. This study, using some of the data on [9], focuses on evaluating some shallow classifier to the sleep stages classification problem. This study evaluates five classifiers: SVM, Neural Network, Classification Tree, k-Nearest Neighborhood (k-NN), and Naive Bayes. Experiment result shows that neural network gives best performance for sleep stage classification problem. Compared to the SVM (the 2-nd rank of accuracy on S000 data), the neural network is also more efficient than SVM in term of computational time and memory requirement.

Original languageEnglish
Title of host publicationICACSIS 2015 - 2015 International Conference on Advanced Computer Science and Information Systems, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages297-301
Number of pages5
ISBN (Electronic)9781509003624
DOIs
Publication statusPublished - 19 Feb 2016
EventInternational Conference on Advanced Computer Science and Information Systems, ICACSIS 2015 - Depok, Indonesia
Duration: 10 Oct 201511 Oct 2015

Publication series

NameICACSIS 2015 - 2015 International Conference on Advanced Computer Science and Information Systems, Proceedings

Conference

ConferenceInternational Conference on Advanced Computer Science and Information Systems, ICACSIS 2015
CountryIndonesia
CityDepok
Period10/10/1511/10/15

Keywords

  • classifier
  • EEG
  • neural network
  • sleep stage classification
  • SVM

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