English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Deep neural networks have played an important role in automatic sleep stageclassification because of their strong representation and in-model featuretransformation abilities. However, class imbalance and individual heterogeneitywhich typically exist in raw EEG signals of sleep data can significantly affectthe classification performance of any machine learning algorithms. To solvethese two problems, this paper develops a generative adversarial network(GAN)-powered ensemble deep learning model, named SleepEGAN, for the imbalancedclassification of sleep stages. To alleviate class imbalance, we propose a newGAN (called EGAN) architecture adapted to the features of EEG signals for dataaugmentation. The generated samples for the minority classes are used in thetraining process. In addition, we design a cost-free ensemble learning strategyto reduce the model estimation variance caused by the heterogeneity between thevalidation and test sets, so as to enhance the accuracy and robustness ofprediction performance. We show that the proposed method can improveclassification accuracy compared to several existing state-of-the-art methodsusing three public sleep datasets.

SleepEGAN: A GAN-enhanced Ensemble Deep Learning Model for Imbalanced Classification of Sleep Stages