EEG Sleep Stages Analysis and Classification Based on Weighed Complex Network Features

被引：54

作者：

Supriya, Supriya ^{[1
]}

Siuly, Siuly ^{[1
]}

Wang, Hua ^{[1
]}

Zhang, Yanchun ^{[1
]}

机构：

[1] Victoria Univ, Coll Engn & Sci, Inst Sustainable Ind & Liveable Cities, Melbourne, Vic 8001, Australia

来源：

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE | 2021年 / 5卷 / 02期

关键词：

Sleep; Electroencephalography; Time series analysis; Complex networks; Standards; Feature extraction; Databases; Complex network; Classification; EEG; EEG signal; feature extraction; Graph Theory; Machine learning; Sleep staging; Weighted Visibility Graph; FREQUENCY; CHANNEL; OSCILLATIONS;

D O I：

10.1109/TETCI.2018.2876529

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Manual staging of sleep data from EEG signals is time consuming, error prone, and resource intensive. These limitations lead to continuous interests of researchers and clinicians toward the automatic sleep stages classification of EEG signals. This paper proposes a scheme for automatic classification of sleep stages using edge strength of visibility graph technique from single-channel EEG signals. To discover the validity of the proposed study with noise, we perform simulation analysis with two different time series named Lorenz and Rossler time series. The proposed technique accomplishes better performance than other related work for the two standard groups of sleep stages: Rechtschaffen and Kales standard and American Academy of Sleep Medicine.

引用

页码：236 / 246

页数：11

共 43 条

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