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脑卒中后抑郁症脑电信号特异性分析.pdf 80页

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ABSTRACT Poststroke depression (PSD), referred to as emotional disorders with main symptoms of depression and interest drops, was among the most common emotional disorders afflicting stroke sufferers, which impacts the rehabilitation progress and functional occurrence seriously. At present, PSD remains no uniform standard for the clinical diagnosis and lack of professional equipment or specific physiological indexes to depression assessment, which lead to high rate of misdiagnosis and missed diagnosis. To explore the feasibility of EEG signals using for the diagnosis of PSD, this thesis acquired resting state EEG signals of three groups (healthy subjects, poststroke non-depression subjects and poststroke depression subjects) for analysis. Non-linear dynamic characteristics (Lemp-Ziv Complexity, LZC and Sample Entropy, SampEn) and brain network features based on PDC (Partial Directed Coherence) were extracted for statistical analysis and network topology analysis respectively with the purpose of exploring specific characteristic changing of EEG signals of PSD subjects. The results of non-linear dynamic analysis showed that the complexity of stroke subjects’ EEG signals was lower than healthy subjects in most of brain regions and the reduction of complexity in PSD subjects’ EEG signals was more significant in frontal lobe, temporal lobe and occipital lobe. The correlation study found that the severity of poststroke depression was correlated with the complexity of EEG signals in frontal region significantly (P<0.05), indicating that the neurons electrical activity of PSD subjects was more ordered and simple than the other two group subjects. Another correlation study found that the time post stroke was correlated wi

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