Automatic sleep stage classification for daytime nap based on hopfield neural network

Author

Xi Chen ; Bei Wang ; Xingyu Wang

Author_Institution

Key Lab. of Adv. Control & Optimization for Chem. Processes, East China Univ. of Sci. & Technol., Shanghai, China

fYear

2013

fDate

25-27 May 2013

Firstpage

2671

Lastpage

2674

Abstract

In this study, automatic method of sleep stage classification for daytime nap is investigated. The ultimate objective is to identify the changing of sleep level during one´s nap. The sleep data is recorded according to the polysomnographic (PSG) measurement. The Electroencephalograph (EEG) is analyzed for sleep stage classification. Totally, 4 parameters are selected and calculated for each 20-second segment of EEG data. The main method is based on Hopfield Neural Network (HNN). The neural network is trained by using standard mode. The sleep stages are classified based on HNN for each consecutive segment. The obtained result showed about 80.6% consistence comparing with the visual inspection. The automatic classification results indicated the changing of sleep level during nap, which can be useful for daytime nap sleep evaluation.

Keywords

Hopfield neural nets; electroencephalography; medical signal processing; sleep; EEG data; HNN; Hopfield neural network; PSG measurement; automatic sleep stage classification method; consecutive segment; daytime nap sleep evaluation; electroencephalograph; polysomnographic measurement; sleep level; visual inspection; Electroencephalography; Feature extraction; Hopfield neural networks; Inspection; Sleep; Standards; Visualization; Daytime Nap; EEG; Hopfield Neural Network; Sleep Stage Determination;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (CCDC), 2013 25th Chinese

Conference_Location

Guiyang

Print_ISBN

978-1-4673-5533-9

Type

conf

DOI

10.1109/CCDC.2013.6561393

Filename

6561393