MPA EEG model-based vigilance level estimation by artificial neural network

Author

Wang, Jiesen ; Wang, Bei ; Wang, Xingyu ; Nakamura, Masatoshi

Author_Institution

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

Volume

2

fYear

2011

fDate

15-17 Oct. 2011

Firstpage

795

Lastpage

799

Abstract

In this paper, the vigilance levels during day time short nap sleep were estimated on the basis of Markov Process Amplitude (MPA) EEG model. The ultimate purpose was to adopt the MPA model to discriminate three levels of vigilance through a simple neural network. A set of parameters were firstly calculated based on MPA EEG model. Secondly, correlation analysis was adopted to extract effective parameters to ensure a small amount of inputs of the artificial neural network. The outputs of artificial neural network were the classified three levels: wakeful, drowsy and sleep. The obtained estimation result showed that the accuracy of wakeful was about 90.0%, drowsy 80.0%, and sleep 93.3%.

Keywords

Markov processes; correlation methods; electroencephalography; medical signal processing; neural nets; neurophysiology; sleep; MPA EEG model based vigilance level estimation; Markov process amplitude EEG model; artificial neural network; correlation analysis; day time short nap sleep; drowsy; wakeful; Artificial neural networks; Brain modeling; Correlation; Electroencephalography; Estimation; Sleep; Training; MPA EEG model; artificial neural network; correlation analysis; power spectrum;

fLanguage

English

Publisher

ieee

Conference_Titel

Biomedical Engineering and Informatics (BMEI), 2011 4th International Conference on

Conference_Location

Shanghai

Print_ISBN

978-1-4244-9351-7

Type

conf

DOI

10.1109/BMEI.2011.6098429

Filename

6098429