Automatic Sleep Stage Scoring Using Time-Frequency Analysis and Stacked Sparse Autoencoders

We developed a machine learning methodology for automatic sleep stage scoring. Our time-frequency analysis-based feature extraction is fine-tuned to capture sleep stage-specific signal features as described in the American Academy of Sleep Medicine manual that the human experts follow. We used ensem...

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Bibliographic Details
Published in:	Annals of biomedical engineering Vol. 44; no. 5; pp. 1587 - 1597
Main Authors:	Tsinalis, Orestis, Matthews, Paul M., Guo, Yike
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.05.2016 Springer Nature B.V
Subjects:	Accuracy Adult Automation Biochemistry Biological and Medical Physics Biomedical and Life Sciences Biomedical Engineering and Bioengineering Biomedicine Biophysics Channels Classical Mechanics Classification Databases, Factual Electroencephalography - instrumentation Electroencephalography - methods Female Frequency analysis Humans Machine Learning Male Manuals Mathematical models Scoring Signal Processing, Computer-Assisted Sleep Sleep Stages Young adults Deep learning Electroencephalography Ensemble learning EEG
ISSN:	0090-6964, 1573-9686, 1573-9686
Online Access:	Get full text
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