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

PeerJ

article

Behrouz Alizadeh Savareh¹  Azadeh Bashiri²  Ali Behmanesh³  Gholam Hossein Meftahi⁴  Boshra Hatef⁴ 

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

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

DOI  :  10.7717/peerj.5247

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

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, 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.

【 授权许可】

CC BY