Feature Eztracting of Weak Signal in Real-Time Sleeping EEG with Approzimate Entropy and Bispectrum Analysis
Electroencephalogram (EEG) is one of the most imp-ortant neuroelectrical signals and is often used to detect brains neuroelectrical dysfunction. However, the analysis of the EEG signal and extraction of features from it has been a challenging task due to the complexity and variability. It is diffcult to recognize different stages of the real-time sleeping EEG signal from single EEG signal automatically. In our study, the features were extracted from single sleeping EEG signal of rats using approximate entropy (ApEn) combined with bispectrum analysis. The results show that ApEn and the maximal amplitude of bispectrum can extract various features in different sleeping EEG stages. The bispectrum can detect the phase coupling among different stages in sleeping EEG signal. The ApEn with change of bispectrums frequency and maximal amplitude of bispectrum can be effectively applied to automatically compartmentalize real-time sleeping EEG signal into different stages, which is significant to automatic EEG analyze and intelligent diagnose of brain diseases. The results also provide a new way of features extraction for other non-stationary signals in real time.
sleeping signal recognition feature eztracting of weak signal approzimate entropy bispectrum analysis
Yuerong Wang Wei Wang Yuelei Liu Degui Wang Baowei Liu Yanjun Shi Ping Gao
Institute of Biomedical Engineering,School of Information Science and Engineering Lanzhou University 222 Southern Tianshui road,Lanzhou,Gansu Province,China,730000
国际会议
北京
英文
1-4
2009-06-11（万方平台首次上网日期，不代表论文的发表时间）