Abstract
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.
Funder
National Natural Science Foundation of China
CAMS Innovation Fund for Medical Science
Beijing Natural Science Foundation
Subject
General Biochemistry, Genetics and Molecular Biology,Medicine (miscellaneous)
Reference40 articles.
1. Feature selection for sleep/wake stages classification using data-driven methods;Zoubek;Biomed. Signal Process. Control,2007
2. DeepSleepNet: A Model for Automatic Sleep Stage Scoring Based on Raw Single-Channel EEG;Supratak;IEEE Trans. Neural Syst. Rehabil. Eng.,2017
3. A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects;Hobson;Electroencephalogr. Clin. Neurophysiol.,1969
4. Iber, C., Ancoli-Israel, S., Chesson, A.L., and Quan, S.F. (2007). The AASM Manual for the Scoring of Sleep and Associated Events, American Academy of Sleep Medicine.
5. Automatic sleep stages classification using optimise flexible analytic wavelet transform;Taran;Knowl.-Based Syst.,2020