- Journal Article
Rights / licenseCreative Commons Attribution 4.0 International
The classification of sleep stages is the first and an important step in the quantitative analysis of polysomnographic recordings. Sleep stage scoring relies heavily on visual pattern recognition by a human expert and is time consuming and subjective. Thus, there is a need for automatic classification. In this work we developed machine learning algorithms for sleep classification: random forest (RF) classification based on features and artificial neural networks (ANNs) working both with features and raw data. We tested our methods in healthy subjects and in patients. Most algorithms yielded good results comparable to human interrater agreement. Our study revealed that deep neural networks (DNNs) working with raw data performed better than feature-based methods. We also demonstrated that taking the local temporal structure of sleep into account a priori is important. Our results demonstrate the utility of neural network architectures for the classification of sleep. Show more
Journal / seriesFrontiers in Neuroscience
Pages / Article No.
PublisherFrontiers Research Foundation
Subjectdeep learning; sleep; EEG; automatic scoring; random forest; artificial neural networks; features; raw data
Organisational unit03654 - Riener, Robert / Riener, Robert
03659 - Buhmann, Joachim M. / Buhmann, Joachim M.
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