Paper Info
Title
Feature Selection Method for Automatic Sleep Staging based on Genetic Algorithm
Abstract
Automatic sleep staging monitoring with high accuracy is an effective sleep assessment method and can assist in the treatment of sleep problems. However, multi-domain feature extraction in automatic sleep staging may lead to too many feature parameters, which may cause “dimension curse” in classification. Based on this, genetic algorithm is applied to feature selection of automatic sleep staging. After extracting multiple features from multiple domains of sleep EEG, genetic algorithm is used as a feature selection method to select a feature subset with good resolution, remove redundant features, and improve the efficiency and accuracy of the classifier. The experimental results show that the performance of different classifiers improved after the feature selection of genetic algorithm. As a feature selection method, genetic algorithm can improve the classification accuracy of automatic sleep staging algorithm.
Year
DOI
Venue
2021
10.1109/CIS54983.2021.00011
2021 17th International Conference on Computational Intelligence and Security (CIS)
Keywords
DocType
ISBN
Automatic Sleep Staging,Feature Selection,Evolutionary Optimization Algorithm,Genetic Algorithm
Conference
978-1-6654-9490-8
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Weilun Yu100.68
Dazhi Jiang28418.57
Huang, Yu354.54
Jiali Lin401.01