AUTHOR=Bresch Erik , Großekathöfer Ulf , Garcia-Molina Gary 

TITLE=Recurrent Deep Neural Networks for Real-Time Sleep Stage Classification From Single Channel EEG

JOURNAL=Frontiers in Computational Neuroscience

VOLUME=12

YEAR=2018

URL=https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2018.00085

DOI=10.3389/fncom.2018.00085

ISSN=1662-5188

ABSTRACT=<p><bold>Objective:</bold> We investigate the design of deep recurrent neural networks for detecting sleep stages from single channel EEG signals recorded at home by non-expert users. We report the effect of data set size, architecture choices, regularization, and personalization on the classification performance.</p><p><bold>Methods:</bold> We evaluated 58 different architectures and training configurations using three-fold cross validation.</p><p><bold>Results:</bold> A network consisting of convolutional (CONV) layers and long short term memory (LSTM) layers can achieve an agreement with a human annotator of Cohen's Kappa of ~0.73 using a training data set of 19 subjects. Regularization and personalization do not lead to a performance gain.</p><p><bold>Conclusion:</bold> The optimal neural network architecture achieves a performance that is very close to the previously reported human inter-expert agreement of Kappa 0.75.</p><p><bold>Significance:</bold> We give the first detailed account of CONV/LSTM network design process for EEG sleep staging in single channel home based setting.</p>