Journal
FRONTIERS IN COMPUTATIONAL NEUROSCIENCE
Volume 12, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fncom.2018.00085
Keywords
deep learning; recurrent networks; EEG; sleep staging; hypnogram
Ask authors/readers for more resources
Objective: 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. Methods: We evaluated 58 different architectures and training configurations using three-fold cross validation. Results: 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 similar to 0.73 using a training data set of 19 subjects. Regularization and personalization do not lead to a performance gain. Conclusion: The optimal neural network architecture achieves a performance that is very close to the previously reported human inter-expert agreement of Kappa 0.75. Significance: We give the first detailed account of CONV/LSTM network design process for EEG sleep staging in single channel home based setting.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available