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PeerJ. 2018 Jul 25;6:e5247. doi: 10.7717/peerj.5247. eCollection 2018.

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

Author information

Student Research Committee, School of Allied Medical Sciences, Shahid Beheshti University of Medical Scinces, Tehran, Iran.
Health Information Management Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.
Student Research Committee, School of Health Management and Information Sciences Branch, Iran University of Medical Sciences, Tehran, Iran.
Neuroscience Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran.



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.


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.


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, the 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.


Artificial neural network; Machine learning; Neighboring component analysis; Sleep scoring; Support vector machine; Wavelet tree analysis

Conflict of interest statement

The authors declare that they have no competing interests.

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