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

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 sta

SUBMITTER: Alizadeh Savareh B 

PROVIDER: S-EPMC6064207 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature