Nonlinear Research On Depression Electroencephalogram And Construction Of Diagnosis Model

Depression is a common mental disease, charactered mainly as a low and pessimistic mood, even so severe as to make some patient suicide. Based on these classification and analysis of its symptoms, background, diagnosis, tools and evaluation criteria in this dissertation, it has been found that not only the number of patients have been increasing, but also the age, scope and industry of their onset have been gradually expanding, so it brings the heavy burden to our society and family. However, the professional engaged in its diagnosis and evaluate are a big shortage, and its diagnosis and evaluation depend on more subjective factors, which lead easily to misdiagnosis and missed diagnosis. Therefore, it is urgent to improve its diagnostic accuracy and efficiency. Based on the synthesis and analysis of its research status, analysis methods and its existing achievements, it has been found that an important reason for the poor accuracy and low efficiency of diagnosis is the lack of objective quantitative biological diagnostic indexes and scientific effective diagnosis model.In view of this situation, the principle and method of nonlinear dynamic are used in this dissertation based on the existing academic works to research in EEG activity, specificity, complexity of depression and the diagnosis model of depression for obtaining some objective biological indicators. The main research contents and results are as follows.1. The research to the EEG activity of depression by proposing a new calculation method called the Power Spectral Entropy. The electrical activity of the brain is the macroscopic effects of various electrical activities of a large number of brain cells. Its active degree and region is closely related with brain function and state. Therefore, it also represents a kind of brain function state, and can reflect brain to be in disease or not. Based on this, the electrical activity strength of depression EEG is studied in contrast to normal healthy human, as well as its biological significance. Inspired by the information entropy of nonlinear dynamics, a new calculation method called the Power Spectrum Entropy is proposed based on the division of time series power spectrum. Namely according to the different division of power spectrum to calculate the power spectrum entropy of EEG time series, the intensity information of EEG signal is obtained, and its correctness is verified by its simulation experiment. Then, this method is used in calculating, analyzing and comparing the EEG signals of depression patients and normal people. Finally, its significance test is carried out by using its hypothesis testing. The results show that the power spectral entropy can be used as an effective physical parameter in measuring brain areas activity, and it can play an important role in diagnosis of brain mental disorder.2. The study on the EEG peculiarity of depression by presenting a new calculation method called the State Distribution Entropy. Peculiarity refers initially to the property of which a creature has some features and others don’t have these features. Later, it is introduced into data mining fields, referring to the data characteristic to be the most abnormal property. The specificity of depression EEG reflects that the sick state in its brain is significantly different from the healthy people, and can deeply reveal the essential characteristics of this disease. Therefore, in order to study this specificity of depression EEG, a computational method called the State Distribution Entropy is proposed for describing and charactering the brain electrical activity distribution. Through using the different state distribution parameter to calculate the different EEG state distribution entropy, we can acquire the peculiar information of analyzing the EEG signal activities. It is studied in many aspects by simulation test, some useful results is obtained. Then, this method is used to calculate, analyze and discuss the EEG significant difference of depression patients and normal people. Finally, its significance test is carried out by using the hypothesis testing. The experiment show that the State Distribution Entropy can characterize some distribution abnormal state of brain electrical activity, can provide peculiar information reflecting its abnormal activities, and can be used as an effective physical parameter in measuring brain electrical activity state abnormal distribution and analyzing EEG peculiarity, and it can play an important role in diagnosis of brain mental disorder.3. It is studied on the EEG complexity of depression by putting forward a new LZ complexity calculation method based on the first order difference multi-scale and multi-valued coarse-grain. EEG have contained a lot of multi-level and multi-side information, if some diagnostic information or pathological information in it need to completely analyze, many discussion from many angles need to carry out. Therefore, these depression EEG are studied from the complexity aspect. This paper proposes a based first order differential multi-scale and multi-valued coarse-grained LZ complexity calculation method, and the principle and calculation steps of the method are described in detail in the paper. Then this method is applied to study on the EEG of depression. The results show that the association of these simultaneous abnormal electrical activity at a certain aspect on multiple brain areas suggest some depression pathogenesis or some pathogenic mechanism. Finding specificity brain physiological function and mechanism of depression from these abnormal measures and its relation is an important role and positive significance for us to study the pathological mechanism and etiology of depression. And it brings us an new idea and research prospect for the diagnosis and assessment of depression.4. It has been attempted to construct the EEG diagnosis model of depression based on the Complexity Spectrum. The diagnosis and assessment of depression are carried out generally by using some scales and questionnaires, using some ways of inquiry and talking, depending mainly on doctor’s personal experience, so far not using objective quantitative diagnosis pattern. Although a few biological diagnostic parameters are proposed in some research literatures, they can still not be applied to clinical practice. Therefore, this paper attempts to put forward a nonlinear diagnosis model based on the Complexity Spectrum inspired by nonlinear science theory to provide an new diagnostic mode and research ideas for these existing diagnosis modes, has offered the method and steps of structuring diagnosis model, and has provided the method and steps of determining these parameters. Finally, the example simulation has been performed for EEG diagnosis model, and the simulation model diagram has been obtained from its results.This thesis makes a comprehensive study on the EEG abnormal phenomenon of depression from the activity, specificity and complexity of EEG, and discusses its possible biological significance hidden in it, finally constructs a diagnostic model of multi-index system. These works have a certain reference value and significance either for theoretical research or clinical practice.