Study On The Nonlinear Dynamical Methods Used For Rehabilition Monitoring Of Craniocerebral Injury

Cerebrum is the most complicated physiological organ of the human body. It dominates people's thoughts and behaviors, and it is the nerve center of the human body to control emotions and functions of vegetative nerve. Those nervous activities are reflected in the changes of the electroencephalogram(EEG) signals which can be collected from cerebral cortex by electrodes. The degrees of congnition and consciousness disturbance caursed by craniocerebral injury can be described by the values of the parameters of the EEG. In this study, the EEG signals of normal and patient groups in different states are collected as original data, and the nonlinear dynamical parameters, correlation dimension D2 and complexity measure C k, are selected to describe the EEG signals. The aim of this study is to seek the numerical differences of the nonlinear dynamical parameters between the normal group and patient group, is to search the changing pattern of the parameters in different treatment and rehabilition period of patient. The results can be used as a reference to evaluate the cerebral congnition and consciousness disturbance functions, to diagnose the cerebral damages and diseases, and to monitor the healing process of craniocerebral injury in clinics.In this study, the EEG signals of all groups collected in different states are as study subjects, the nonlinear dynamics is as the basic theory, and the theories and methods of signal processing are as the basic means. The computing methods of the nonlinear parameters, preprocessing methods of the EEG signal, computing methods of the EEG signal and the analyzing methods of the data are systematically studied. The main contents of this paper are follows:(1) Based on the studies of the nonlinear dynamics, the characteristics and computing methods of the correlation dimension and the Kolmogorov complexity measures are discussed. After the selection methods of the reconstruction parameters of phase-space and the computation parameters of nonlinear indexes are compared and analyzed, the reference standards to select those parameters are presented. At last, the complete programs are designed to calculate the nonlinear indexes.(2) The chaotic characteristics of the EEG signals are certified by many methods, such as the reconstruction of its strange attractor, scattered plots, power spectrum and numerical recognition. All indicate that the EEG signal is nonlinear signal and it can be described by the nonlinear indexes.(3) The schemes of thought model are designed. All original EEG signals are collected in the schemes. By the digital filtering and wavelet transforming, the EEG noises, such as power-frequency interference, baseline drift, EMG, ECG and EOG artifacts, are removed from the original EEG signals. Based on the thought burst theories, the thought EEG signals are synchronously separated. By the effective selections of the EEG signals, the experiment data are obtained. This process improves the reliability and accuracy of the experiment results.(4) In the calculating process of the correlation dimension D2 , because of the high density of the reconstructed phase-space, the traditional G-P algorithm is improved. By the modified algorithms, the computing speeds of the correlation dimension D2 are increased and the stabilities of the computing results are maintained. The modified algorithms are deduced from the original G-P algorithm and verified by numerical simulations. The high-order complexity measure is introduced to represent the complexity of the EEG signals. By this means, the missing information in the charactering process of the EEG sequences is reduced and the accuracy of the complexity measure to represent the EEG signals is increased.(5) The correlation dimension D2 and complexity measure C k of all samples in different states are calculated. The computing results are analyzed and compared in different thought states and cerebral areas. According to the results, the correlation dimension D2 and complexity measure C k for different samples and states can be remarkably differentiated.The main original contributions of this paper are as follows:(1) The nonlinear dynamic parameters of EEG signals are ulitilized to describe the degrees of disturbance of consiousness for patients in craniocerebral injury. Comparing with the traditional evaluation method which is subjective because it must be fulfilled by clinical doctor, the method is objective, noninvasive, numerical and operable.(2) The schemes of thought model for the EEG signals collection are designed. For test group, 5 thought tasks are designed and 8 thought tasks are designed for experiment groups.(3) By the methods and tools of signal processing, the EEG noises and artifacts are removed from the original EEG signals. Based on the thought burst theories, the thought EEG signals are synchronously separated. By those processes, the reliability and accuracy of the experiment results are improved. According to the high density of the reconstructed phase-space, the traditional G-P algorithm is improved. After the limited interval timeτis selected, the stabilities of the computing results can be maintained.(4) The statistic results of the correlation dimension D2 and complexity measure C k of the EEG signals in different groups and states. By the results, the statistic characteristics of the correlation dimension D2 are similar with the characteristics of the complexity measure C k. The values of the correlation dimension D2 and complexity measure C k are large in the states of complex thought activities. The values of the correlation dimension D2 and complexity measure C k for normal group are larger for patient group. The statistic results of the correlation dimension D2 and complexity measure C k of the EEG signals for all samples are different between right and left side cerebral areas. The correlation dimension D2 and the complexity measure C k of EEG signals for patients in craniocerebral injury are changed with the processes of the treatment and rehabilition. The results can be preliminarily used as a reference to evaluate the cerebral functions, to diagnose the cerebral damages and diseases, and to monitor the healing process of encephalopathy in clinics.