Application Of Nonlinear Dynamics Theory To Biomedicine

This paper mainly deals with a number of issues of nonlinear dynamics theory in the application of biomedical engineering. It contains nonlinear dynamics quantitative criteria's applicability and limitations of biological signals, biophysical model, nonlinear dynamics analysis of human brain depth intelligent activities and children with epilepsy.Firstly, the comparative studies of phase graph, bifurcation graph, power spectra, correlation dimension and Lyapunov exponent of EEG show that chaotic pattern of the dynamics model takes on alternation between period and chaos, and relevant to double-periodic bifurcation, Hopf bifurcation, and reverse bifurcation; to further support the view that chaos exist in EEG signals. And the result of prediction of RBF neural network and NLCP indicate the effect of RBF neural network prediction is better than that of nonlinear prediction; the method of NLCP is adaptive to time series with strong periodic components.Then, according to phase space reconstruct technique from one-dimensional and multi-dimensional time series, the quantitative criterion and rule of system chaos which combine neural network, analyses, computations and sort are conducted on EEG signals of five kinds of human consciousness activities. The author finds that statistic results of the determinism computation in time series indicate that chaos characteristic may lie in human consciousness activities, and central tendency measure is consistent with phase graph, so it may become one division way of attractor; approximate entropy of different subjects exists discrepancy; correlation dimension and Lyapunov exponent of different consciousness activities indicate that attractors of human activities are all fractional; nonlinear quantitative criterion and rule, which unites neural network, can distinguish different styles of consciousness activity in principle, and the result of sortation takes on that consciousness activity type of arithmetic is distinguished more easily than that of abstract.Finally, based on C-C computation of EEG, the comparative studies of phase graph, approximate entropy, power spectra, correlation dimension indicate that embeded dimension and signal delay of EEG is closely linked with the state of the brain. And phase graph, approximate entropy, Lyapunov exponent, power spectra, correlation dimension reflects the whole dynamic characteristics of the brain. The potential dynamics change before epilepsy provides a strong theoretical support of developing epilepsy warning equipment.