Feature Transformation Based on Stacked Sparse Autoencoders for Sleep Stage Classification

Shirin Najdi, Ali Abdollahi Gharbali, Jose Manuel Fonseca

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

13 Citations (Scopus)

Abstract

In this paper a deep learning based dimension reduction, feature transformation and classification method is proposed for automatic sleep stage classification. In order to enhance the feature vector, before feeding it to the deep network, a discriminative feature selection method is applied for removing the features with minimum information. Two-layer Stacked Sparse Autoencoder together with Softmax classifier is selected as the deep network model. The performance of the proposed method is compared with Softmax and k-nearest neighbour classifiers. Simulation results show that proposed deep learning structure outperformed others in terms of classification accuracy.
Original languageEnglish
Title of host publicationTechnological Innovation for Smart Systems
Subtitle of host publication8th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2017
EditorsL. M. Camarinha-Matos, M. Parreira-Rocha , J. Ramezani
Place of PublicationCham
PublisherSpringer
Pages191-200
ISBN (Electronic)978-3-319-56077-9
ISBN (Print)978-3-319-56076-2
DOIs
Publication statusPublished - 2017
Event8th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems (DoCEIS) - Costa de Caparica, Portugal
Duration: 3 May 20175 May 2017

Publication series

NameIFIP Advances in Information and Communication Technology
PublisherSpringer
Volume499
ISSN (Print)1868-4238

Conference

Conference8th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems (DoCEIS)
CountryPortugal
CityCosta de Caparica
Period3/05/175/05/17

Keywords

  • Sleep stage classification
  • Deep learning
  • Stacked Sparse Autoencoders
  • Feature transformation

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