SleepEGAN: A GAN-enhanced ensemble deep learning model for imbalanced classification of sleep stages.

Deep neural networks have played an important role in the automatic classification of sleep stages due to 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 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, to enhance the accuracy and robustness of prediction performance. We show that the proposed method improves classification accuracy compared to several existing state-of-the-art methods. The overall classification accuracy and macro F1-score obtained by our SleepEGAN method on three public sleep datasets are: Sleep-EDF-39: 86.8% and 81.9%; Sleep-EDF-153: 83.8% and 78.7%; SHHS: 88.0% and 82.1%. • This paper develops a new GAN-enhanced ensemble deep learning model, called SleepEGAN, for the imbalanced classification of sleep stages. • It targets to solve two problems commonly existing in sleep or EEG signal-related data — the class imbalance and individual heterogeneity of EEG signals. • It proposes a new EGAN model that can effectively and efficiently generate EEG signals for small classes of sleep stages, and it develops a cost-free ensemble algorithm that can solve the problem of individual heterogeneity of EEG signals. • The proposed method meets the immediate needs of practitioners for seeking an automatic toolbox to classify the sleep stages in the sleep data which are imbalanced and heterogeneous. • It can be conveniently used for automated sleep monitoring and promotes modern wearable technologies. [ABSTRACT FROM AUTHOR]