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A Few-Shot Learning-Based EEG and Stage Transition Sequence Generator for Improving Sleep Staging Performance

ORCID
0000-0002-3062-379X
Affiliation
Beijing Institute of Technology, School of Automation, Beijing 100081, China
You, Yuyang;
Affiliation
Beijing Institute of Technology, School of Automation, Beijing 100081, China
Guo, Xiaoyu;
Affiliation
Department of Electrical and Computer Engineering, Technical University of Munich, 80333 Munich, Germany
Zhong, Xuyang;
Affiliation
Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100193, China
Yang, Zhihong

In this study, generative adversarial networks named SleepGAN are proposed to expand the training set for automatic sleep stage classification tasks by generating both electroencephalogram (EEG) epochs and sequence relationships of sleep stages. In order to reach high accuracy, most existing classification methods require substantial amounts of training data, but obtaining such quantities of real EEG epochs is expensive and time-consuming. We introduce few-shot learning, which is a method of training a GAN using a very small set of training data. This paper presents progressive Wasserstein divergence generative adversarial networks (GANs) and a relational memory generator to generate EEG epochs and stage transition sequences, respectively. For the evaluation of our generated data, we use single-channel EEGs from the public dataset Sleep-EDF. The addition of our augmented data and sequence to the training set was shown to improve the performance of the classification model. The accuracy of the model increased by approximately 1% after incorporating generated EEG epochs. Adding both the augmented data and sequence to the training set resulted in a further increase of 3%, from the original accuracy of 79.40% to 83.06%. The result proves that SleepGAN is a set of GANs capable of generating realistic EEG epochs and transition sequences under the condition of insufficient training data and can be used to enlarge the training dataset and improve the performance of sleep stage classification models in clinical practice.

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