Sleep Stage Classification via Ensemble and Conventional Machine Learning Methods using Single Channel EEG Signals

Hamza Osman Ilhan; Gokhan Bilgin

doi:10.18201/ijisae.2017533859

Authors

Hamza Osman Ilhan Yıldız Technical University
Gokhan Bilgin Yildiz Technical University, Dpt. of Computer Engineering http://orcid.org/0000-0002-5532-477X

DOI:

https://doi.org/10.18201/ijisae.2017533859

Keywords:

Sleep-stage classification, EEG, machine learning, ensemble learning, PhysioNet

Abstract

Sleep-stages play important roles in the diagnosis of the sleep disorders and the sleep-related illnesses. In this sense, accurate identification of the sleep-stages is a necessity for more robust and e client diagnosis systems. Several traditional machine-learning and pattern recognition algorithms are deployed on modern computer aided diagnosis systems. However, current results are not as satisfactory as expected. In the last two decade, a new concept has emerged with ‘ensemble learning’ title. It has attracted the attention of many researchers from various disciplines. In this study, several ensemble-learning methods are utilized and inspected on EEG signals for sleep-stage classification. Conventional machine-learning methods are also performed in same testing phase to report comparative results. Additionally, methods are evaluated in two different scenarios; subject specific and independent. Study proves that combination of DTs and SVMs in Bagging theorem surpasses all of the conventional methods used in the experiments. Moreover, test trials reveal that both conventional and ensemble models need to be improved for subject independent scenario which is more essential case in the development of independent computer based diagnosis systems.

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Author Biography

Gokhan Bilgin, Yildiz Technical University, Dpt. of Computer Engineering

Gokhan Bilgin received B.Sc., M.Sc., and Ph.D. degrees in Electronics and Telecommunication engineering from Yildiz Technical University (YTU), Istanbul, Turkey, in 1999, 2003, and 2009, respectively. He worked as post-doctorate researcher at Computer Aided Diagnosis and Knowledge Discovery Laboratory (CADKD) of the Department of Computer and Information Science in Indiana University–Purdue University Indianapolis (IUPUI). He is currently working as Assistant Professor in Department of Computer Engineering at Yildiz Technical University. His research interests are in the areas of image and signal processing, machine learning, and pattern recognition with applications to biomedical engineering and remote sensing. He is the founder of Signal and Image Processing Laboratory (SIMPLAB) in Department of Computer Engineering at Yildiz Technical University.

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