Automatic EOG and EMG artifact removal method for sleep stage classification

Ali Abdollahi Gharbali, José Manuel Fonseca, Shirin Najdi, Tohid Yousefi Rezaii

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

In this paper, a new algorithm is proposed for artifact removing of sleep electroencephalogram (EEG) with application in sleep stage classification. Rather than other works which used artificial noise, in this study real EEG data contaminated with electro-oculogram (EOG) and electromyogram (EMG) are used for evaluating the proposed artifact removal algorithm’s efficiency using classification accuracy. The artifact detection is performed by thresholding the EEG-EOG and EEG-EMG cross correlation coefficients. Then, the segments considered contaminated are denoised by normalized least-mean squares (NLMS) adaptive filtering technique. Using a single EEG channel, four sleep stages consisting of Awake, Stage1 + REM, Stage 2 and Slow Wave Stage (SWS) are classified. A wavelet packet (WP) based feature set together with artificial neural network (ANN) are deployed for sleep stage classification purpose. Simulation results show that artifact removed EEG allows a classification accuracy improvement of around 14%.

Original languageEnglish
Title of host publicationTechnological Innovation for Cyber-Physical Systems - 7th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2016, Proceedings
PublisherSpringer New York LLC
Pages142-150
Number of pages9
Volume470
ISBN (Electronic)978-3-319-31165-4
ISBN (Print)978-3-319-31164-7
DOIs
Publication statusPublished - 2016
Event7th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2016 - Costa de Caparica, Portugal
Duration: 11 Apr 201613 Apr 2016

Publication series

NameIFIP Advances in Information and Communication Technology
Volume470
ISSN (Print)18684238

Conference

Conference7th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2016
CountryPortugal
CityCosta de Caparica
Period11/04/1613/04/16

Keywords

  • Adaptive filtering
  • Artifact detection
  • Artifact removing
  • Sleep stage classification
  • Wavelet packet

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