Feature Transformation Based on Stacked Sparse Autoencoders for Sleep Stage Classification

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.
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Submitted on : Monday, November 6, 2017 - 3:30:00 PM
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Shirin Najdi, Ali Gharbali, José Fonseca. Feature Transformation Based on Stacked Sparse Autoencoders for Sleep Stage Classification. 8th Doctoral Conference on Computing, Electrical and Industrial Systems (DoCEIS), May 2017, Costa de Caparica, Portugal. pp.191-200, ⟨10.1007/978-3-319-56077-9_18⟩. ⟨hal-01629592⟩



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