Automatic Human Sleep Stage Scoring Using Deep Neural Networks

• Published in: Frontiers in neuroscience Vol. 12; p. 781
• Main Authors: Malafeev, Alexander, Laptev, Dmitry, Bauer, Stefan, Omlin, Ximena, Wierzbicka, Aleksandra, Wichniak, Adam, Jernajczyk, Wojciech, Riener, Robert, Buhmann, Joachim, Achermann, Peter
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 06.11.2018; Frontiers Media S.A
• Subjects: Agreements; Algorithms; Artificial intelligence; artificial neural networks; automatic scoring; Automation; Classification; Decision theory; deep learning; EEG; Electroencephalography; Eye movements; Learning algorithms; Machine learning; Medicine; Neural networks; Neuroscience; Pattern recognition; Psychiatry; random forest; Sleep; Sleep disorders; Poland; Switzerland; sleep; artificial neural networks; deep learning; features; EEG; raw data; automatic scoring; random forest
• ISSN: 1662-453X; 1662-4548; 1662-453X
• DOI: 10.3389/fnins.2018.00781

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.