Complex-valued unsupervised convolutional neural networks for sleep stage classification

2018 ◽  
Vol 164 ◽  
pp. 181-191 ◽  
Author(s):  
Junming Zhang ◽  
Yan Wu
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Sleep Stage ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Complex Valued

scholarly journals Automated sleep stage classification in sleep apnoea using convolutional neural networks

2021 ◽  
Vol 26 ◽  
pp. 100724
Author(s):  
G. Naveen Sundar ◽  
D. Narmadha ◽  
A. Amir Anton Jone ◽  
K. Martin Sagayam ◽  
Hien Dang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Sleep Stage ◽  
Sleep Apnoea ◽  
Stage Classification ◽  
Sleep Stage Classification

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

2018 ◽  
Vol 63 (2) ◽  
pp. 177-190 ◽  
Author(s):  
Junming Zhang ◽  
Yan Wu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Single Channel ◽  
Sleep Stage ◽  
Experimental Results ◽  
Sleep Stages ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Complex Valued ◽  
Learned Features

AbstractMany 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.

scholarly journals Pediatric Sleep Stage Classification Using Multi-Domain Hybrid Neural Networks

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 96495-96505 ◽  
Author(s):  
Yonghoon Jeon ◽  
Siwon Kim ◽  
Hyun-Soo Choi ◽  
Yoon Gi Chung ◽  
Sun Ah Choi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Sleep Stage ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Hybrid Neural Networks ◽  
Pediatric Sleep
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