Complex-valued unsupervised convolutional neural networks for sleep stage classification. (October 2018) - Zhang, Junming; Wu, Yan
Highlights: This study developed a novel sleep stage system based on electroencephalogram. A new unsupervised complex-valued convolutional neural network was proposed. The proposed method can automatically extract features from raw EEG signals. Abstract: Background and objective: Despite numerous deep learning methods being developed for automatic sleep stage classification, almost all the models need labeled data. However, obtaining labeled data is a subjective process. Therefore, the labels will be different between two experts. At the same time, obtaining labeled data also is a time-consuming task. Even an experienced expert requires hours to annotate the sleep stage patterns. More important, as the development of wearable sleep devices, it is very difficult to obtain labeled sleep data. Therefore, unsupervised training algorithm is very important for sleep stage classification. Hence, a new sleep stage classification method named complex-valued unsupervised convolutional neural networks (CUCNN) is proposed in this study. Methods: The CUCNN operates with complex-valued inputs, outputs, and weights, and its training strategy is greedy layer-wise training. It is composed of three phases: phase encoder, unsupervised training and complex-valued classification. Phase encoder is used to translate real-valued inputs into complex numbers. In the unsupervised training phase, the complex-valued K-means is used to learn filters which will be used in the convolution. Results: The classification performances of handcrafted features are compared with those of learned features via CUCNN. The total accuracy (TAC) and kappa coefficient of the sleep stage from UCD dataset are 87% and 0.8, respectively. Moreover, the comparison experiments indicate that the TACs of the CUCNN from UCD and MIT-BIH datasets outperform these of unsupervised convolutional neural networks (UCNN) by 12.9% and 13%, respectively. Additionally, the convergence of CUCNN is much faster than that of UCNN in most cases. Conclusions: The proposed method is fully automated and can learn features in an unsupervised fashion. Results show that unsupervised training and automatic feature extraction on sleep data are possible, which are very important for home sleep monitoring.