Bi-directional Long Short-Term Memory using Quantized data of Deep Belief Networks for Sleep Stage Classification

Intan Nurma Yulita, Mohamad Ivan Fanany, Aniati Murni Arymuthy

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

19 Citations (Scopus)

Abstract

The study examines the use of quantization to be applied to Bi-directional Long Short-Term Memory (Bi-LSTM), a combination of the two called qBi-LSTM. Quantization used comes from Deep Belief Networks (DBN). It selected DBN for its superiority as a generative model of Deep Learning in producing an optimal artificial feature. Development of qBi-LSTM is expected to improve the performance of Bi-LSTM and also provide efficient time. The qBi-LSTM test is applied for sleep stage classification on St. Vincent's University Hospital / University College Dublin's Sleep Apnea Database. The result shows that qBi-LSTM has the highest performance compared to Bi-LSTM and DBN with precision, recall and F-measure values of 86.00%, 72.10%, and 75.27%. The best qBi-LSTM performance is to classify Stage 2 but still fails to classify the stage of REM (Rapid Eye Movement).

Original languageEnglish
Title of host publication2nd International Conference on Computer Science and Computational Intelligence, ICCSCI 2017
EditorsWdodo Budiharto, Dewi Suryani, Lili A. Wulandhari, Andry Chowanda, Alexander A.S. Gunawan, Novita Hanafiah, Hanry Ham, Meiliana
PublisherElsevier B.V.
Pages530-538
Number of pages9
ISBN (Print)9781510849914
DOIs
Publication statusPublished - 1 Jan 2017
Event2nd International Conference on Computer Science and Computational Intelligence, ICCSCI 2017 - Bali, Indonesia
Duration: 13 Oct 201714 Oct 2017

Publication series

NameProcedia Computer Science
Volume116
ISSN (Electronic)1877-0509

Conference

Conference2nd International Conference on Computer Science and Computational Intelligence, ICCSCI 2017
CountryIndonesia
CityBali
Period13/10/1714/10/17

Keywords

  • Bi-directional Long Short-Term Memory
  • Deep Belief Networks
  • Long Short-Term Memory
  • Sleep Stage Classification

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