Comparative analysis of different characteristics of automatic sleep stages

Dechun Zhao; Yi Wang; Qiangqiang Wang; Xing Wang

doi:10.1016/j.cmpb.2019.04.004

Comparative analysis of different characteristics of automatic sleep stages

Comput Methods Programs Biomed. 2019 Jul:175:53-72. doi: 10.1016/j.cmpb.2019.04.004. Epub 2019 Apr 6.

Authors

Dechun Zhao¹, Yi Wang², Qiangqiang Wang², Xing Wang³

Affiliations

¹ Chongqing Engineering Laboratory of Digital Medical Equipment and Systems, Chongqing University of Posts and Telecommunications, Chongqing 400065, China. Electronic address: zhaodc@cqupt.edu.cn.
² Chongqing Engineering Laboratory of Digital Medical Equipment and Systems, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
³ College of Biomedical Engineering, Chongqing University, Chongqing 400044, China.

PMID: 31104715
DOI: 10.1016/j.cmpb.2019.04.004

Abstract

Background and objective: With the acceleration of social rhythm and the increase of pressure, there are various sleep problems among people. Sleep staging is an important basis for the diagnosis of sleep disorders and other related diseases. The process of automatic staging of sleep is mainly divided into three core steps: data preprocessing, feature extraction, and classification. Accurate analysis of the features of sleep electroencephalogram (EEG) signals is not only helpful to improve the accuracy of sleep staging, but also help people to understand their sleep status.

Methods: This paper focuses on the analysis of EEG features during sleep staging, and reviews many feature extraction methods and classification methods for sleep staging and sums up these algorithms used in literatures and its staging results. Besides, this paper lists a total of 22 features based on time domain, time-frequency, and nonlinear analysis methods, including kurtosis, skewness, Hjorth parameters, and standard deviations, wavelets energy; sample entropy (SampEn), fuzzy entropy, Tsallis entropy, fractal dimension (FD), complexity. The data set comes from EDF database. Wavelet transform (WT) and support vector machine (SVM) are used to achieve the sleep staging based on single-channel EEG signal. And the characteristic feature data was analyzed by ANOVA.

Results: By comparison, the SampEn, fuzzy entropy, FD and complexity can achieve ideal sleep staging. The highest accuracy of sleep staging is 85.93%. The FD and complexity are simpler than the entropy value, but their accuracies are lower. Furthermore, the distribution of these methods in different sleep period is more significant than others, which is content with the results of sleep staging.

Conclusion: In a word, due to the non-stationary and non-linear characteristics of EEG signals, time domain and time-frequency analysis methods all have some limitations. Nonlinear analysis was more effective and practical for the analysis of sleep EEG.

Keywords: ANOVA; Automatic sleep staging; Classification; Data preprocessing; Feature extraction.

Publication types

Comparative Study

MeSH terms

Algorithms
Analysis of Variance
Electroencephalography*
Entropy
Fractals
Fuzzy Logic
Humans
Pattern Recognition, Automated
Reproducibility of Results
Signal Processing, Computer-Assisted*
Sleep / physiology*
Sleep Stages*
Support Vector Machine
Wavelet Analysis