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Automatic EOG and EMG Artifact Removal Method for Sleep Stage Classification

Ali Gharbali 1, 2 José Fonseca 1, 2 Shirin Najdi 1, 2 Tohid Rezaii 3
1 CA3 - Computational Intelligence Research Group
CTS - Centre of Technology and Systems, FCT NOVA - Faculdade de Ciências e Tecnologia
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 %.
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Ali Gharbali, José Fonseca, Shirin Najdi, Tohid Rezaii. Automatic EOG and EMG Artifact Removal Method for Sleep Stage Classification. 7th Doctoral Conference on Computing, Electrical and Industrial Systems (DoCEIS), Apr 2016, Costa de Caparica, Portugal. pp.142-150, ⟨10.1007/978-3-319-31165-4_15⟩. ⟨hal-01438238⟩

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