MPA EEG Model-based Vigilance Level Estimation by Artificial Neural Network
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％.
MPA EEG model correlation analysis artificial neural network power spectrum
Jiesen Wang Bei Wang Xingyu Wang Masatoshi Nakamura
Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education,Sc Research Institute of Systems Control, Institute for Advanced Research and Education Saga University
国际会议
上海
英文
797-801
2011-10-15（万方平台首次上网日期，不代表论文的发表时间）