Performance comparison of machine learning techniques in sleep scoring based on wavelet features and neighboring component analysis
A peer-reviewed article of this Preprint also exists.
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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.
Cite this as
2018. Performance comparison of machine learning techniques in sleep scoring based on wavelet features and neighboring component analysis. PeerJ Preprints 6:e27020v1 https://doi.org/10.7287/peerj.preprints.27020v1note This preprint is not peer-reviewed. You may wish to reference the subsequent peer-reviewed version of this article.
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This is a submission to PeerJ for review.
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Competing Interests
The authors declare that they have no competing interests.
Author Contributions
Behrouz Alizadeh Savareh conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, prepared figures and/or tables, authored or reviewed drafts of the paper, approved the final draft.
Azadeh Bashiri performed the experiments, contributed reagents/materials/analysis tools, prepared figures and/or tables, authored or reviewed drafts of the paper, approved the final draft.
Ali Behmanesh contributed reagents/materials/analysis tools, prepared figures and/or tables, authored or reviewed drafts of the paper, approved the final draft.
Gholam Hossein Meftahi contributed reagents/materials/analysis tools, authored or reviewed drafts of the paper, approved the final draft.
Boshra Hatef conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, authored or reviewed drafts of the paper, approved the final draft.
Data Deposition
The following information was supplied regarding data availability:
The raw data used in this study is publicly available on the Physionet website:
https://physionet.org/physiobank/database/sleep-edf/
Matlab code for the implementation of the methods are available as Supplemental Files.
Funding
The authors received no funding for this work.
