An analysis of EEG when acupuncture with wavelet entropy.
Wavelet energy entropy derived from wavelet multi-revolution decomposition, reconstruction and Shannon entropy can signify the complexity of unsteady EEG signals in both time domain and frequency domain. Firstly, the paper gives an introduction of the methods about wavelet energy entropy. Then the EEG signals when acupuncture is analyzed and some conclusions are addressed by using wavelet energy entropy, relative wavelet energy entropy and the time evolution of them.
Li
Nuo
N
School of electronic engineering and automation, Tianjin University, CO 300072, China. nuoli@tju.edu.cn
Wang
Jiang
J
Deng
Bin
B
Dong
Feng
F
D015670:Acupuncture Therapy / Q000379:methods*; D000465:Algorithms; D001921:Brain / Q000502:physiology*; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D008959:Models, Neurological