Performance comparison of machine learning techniques in sleep scoring based on wavelet features and neighboring component analysis

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1 Student Research Committee, School of Allied Medical Sciences, Shahid Beheshti University of Medical Scinces, Tehran, Iran
2 Health Information Management Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
3 Student Research Committee, School of Health Management and Information Sciences Branch, Iran University of Medical Sciences, Tehran, Iran
4 Neuroscience Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran
DOI
10.7287/peerj.preprints.27020v1
Published
Accepted
Subject Areas
Bioinformatics, Neuroscience, Data Mining and Machine Learning
Keywords
Sleep scoring, artificial neural network, Neighboring component analysis, Machine learning, support vector machine, wavelet tree analysis
Copyright
© 2018 Alizadeh Savareh et al.
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
Cite this article
Alizadeh Savareh B, Bashiri A, Behmanesh A, Meftahi GH, Hatef B. 2018. Performance comparison of machine learning techniques in sleep scoring based on wavelet features and neighboring component analysis. PeerJ Preprints 6:e27020v1

Abstract

Introduction: Sleep scoring is an important step in the treatment of sleep disorders. Manual annotation of sleep stages is time-consuming and experience-relevant and, therefore, needs to be done using machine learning techniques. methods: Sleep-edf polysomnography was used in this study as a dataset. Support Vector Machines and Artificial Neural Network performance were compared in sleep scoring using wavelet tree features and neighborhood component analysis. Results: Neighboring component analysis as a combination of linear and non-linear feature selection method had a substantial role in feature dimension reduction. Artificial neural network and support vector machine achieved 90.30% and 89.93% accuracy respectively. Discussion and Conclusion: Similar to the state of the art performance, introduced method in the present study achieved an acceptable performance in sleep scoring. Furthermore, its performance can be enhanced using a technique combined with other techniques in feature generation and dimension reduction. It is hoped that, in the future, intelligent techniques can be used in the process of diagnosing and treating sleep disorders.

Author Comment

This is a submission to PeerJ for review.

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