SleepEGAN: A GAN-enhanced Ensemble Deep Learning Model for Imbalanced
  Classification of Sleep Stages
Xuewei Cheng Ke Huang Yi Zou Shujie Ma
Deep neural networks have played an important role in automatic sleep stage
classification because of their strong representation and in-model feature
transformation abilities. However, class imbalance and individual heterogeneity
which typically exist in raw EEG signals of sleep data can significantly affect
the classification performance of any machine learning algorithms. To solve
these two problems, this paper develops a generative adversarial network
(GAN)-powered ensemble deep learning model, named SleepEGAN, for the imbalanced
classification of sleep stages. To alleviate class imbalance, we propose a new
GAN (called EGAN) architecture adapted to the features of EEG signals for data
augmentation. The generated samples for the minority classes are used in the
training process. In addition, we design a cost-free ensemble learning strategy
to reduce the model estimation variance caused by the heterogeneity between the
validation and test sets, so as to enhance the accuracy and robustness of
prediction performance. We show that the proposed method can improve
classification accuracy compared to several existing state-of-the-art methods
using three public sleep datasets.