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Automatic Human Sleep Stage Scoring Using Deep Neural Networks

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Author Malafeev Alexander, Laptev Dmitry, Bauer Stefan, Omlin Ximena, Wierzbicka Aleksandra, Wichniak Adam, Jernajczyk Wojciech, Riener Robert, Buhmann Joachim, Achermann Peter,
Project Sleep onset and other state transitions: insights from quantitative EEG analysis
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Original article (peer-reviewed)

Journal Frontiers in Neuroscience
Volume (Issue) 12
Page(s) 781
Title of proceedings Frontiers in Neuroscience
DOI 10.3389/fnins.2018.00781

Open Access

URL http://doi.org/10.3389/fnins.2018.00781
Type of Open Access Publisher (Gold Open Access)

Abstract

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.
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