EEG Signal Processing in Anesthesia Feature Extraction of Time and Frequency parameters
Electroencephalogram (EEG) is well established for assessing the functional state of the brain. During the anesthesia, the brain activity level changed dramatically, so we could get the depth of anesthesia estimation from EEG recording. In previous research, most achievements were in the frequency-domain. In this paper, besides the frequency-domain features extraction, we have introduced the concept of Entropy as well as nonlinear feature. The results show that, with the deepening of anerthesia degree, approximate entropy (ApEn), Shannon entropy (SSE)and Lempel - Ziv complexity from EEG signal decrease gradually. Center frequency towards to low frequency, total power shows a rising trend. Largest Lyapunov index also decreases, while the correlation dimension has no clear trend.
EEG Anesthesia Feature Extraction Time and Frequency Analysis Nonlinear Parameter
Zhengqiang Ni Lei Wang Jun Meng Fuming Qiu Jian Huang
College of Electrical Engineering, Zhejiang University Hangzhou, China 310027 Department of Oncology, Second Affiliated Hospital, Zhejiang University School of Medicine Hangzhou,
国际会议
成都
英文
32-36
2011-01-14（万方平台首次上网日期，不代表论文的发表时间）