NDLI: Automatic sleep stage classification of single-channel EEG by using complex-valued convolutional neural network

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Automatic sleep stage classification of single-channel EEG by using complex-valued convolutional neural network

Content Provider	Scilit
Author	Zhang, Junming Wu, Yan
Copyright Year	2017
Abstract	Many systems are developed for automatic sleep stage classification. However, nearly all models are based on handcrafted features. Because of the large feature space, there are so many features that feature selection should be used. Meanwhile, designing handcrafted features is a difficult and time-consuming task because the feature designing needs domain knowledge of experienced experts. Results vary when different sets of features are chosen to identify sleep stages. Additionally, many features that we may be unaware of exist. However, these features may be important for sleep stage classification. Therefore, a new sleep stage classification system, which is based on the complex-valued convolutional neural network (CCNN), is proposed in this study. Unlike the existing sleep stage methods, our method can automatically extract features from raw electroencephalography data and then classify sleep stage based on the learned features. Additionally, we also prove that the decision boundaries for the real and imaginary parts of a complex-valued convolutional neuron intersect orthogonally. The classification performances of handcrafted features are compared with those of learned features via CCNN. Experimental results show that the proposed method is comparable to the existing methods. CCNN obtains a better classification performance and considerably faster convergence speed than convolutional neural network. Experimental results also show that the proposed method is a useful decision-support tool for automatic sleep stage classification.
Related Links	http://www.degruyter.com/downloadpdf/j/bmte.ahead-of-print/bmt-2016-0156/bmt-2016-0156.xml
Ending Page	190
Page Count	14
Starting Page	177
ISSN	00135585
e-ISSN	1862278X
DOI	10.1515/bmt-2016-0156
Journal	Biomedizinische Technik/Biomedical Engineering
Issue Number	2
Volume Number	63
Language	English
Publisher	Walter de Gruyter GmbH
Publisher Date	2018-03-28
Access Restriction	Open
Subject Keyword	Biomedizinische Technik/biomedical Engineering Information Systems Complex-valued Convolutional Neural Network Electroencephalography Feature Extraction Sleep Stage Supervised Learning Journal: Biomedizinische Technik/Biomedical Engineering, Vol- 63
Content Type	Text
Resource Type	Article
Subject	Medicine Biomedical Engineering

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