本文選題：抑郁癥 + 功率譜�。� 參考：《北京工業(yè)大學(xué)》2014年碩士論文

【摘要】：抑郁癥是發(fā)病率較高的情感障礙精神疾病。人腦無刺激狀態(tài)下釋放出的腦電信號能反映大腦的不同狀態(tài)，其中包括異常狀態(tài)，已是臨床上精神疾病檢查的常用工具。首先，本研究希望在抑郁癥病人的腦電信號中發(fā)現(xiàn)與正常人異常的特征，以便深入了解抑郁癥的疾病機理，以及對診斷和治療效果評估有所幫助。接著用3種著名模式識別算法，使用本研究發(fā)現(xiàn)的特征對腦電信號進行分類研究，以檢驗這些特征的分類效果。 本研究對正常人，，抑郁癥未用藥病人和抑郁癥用藥病人這3種人群采樣，形成3組被試。其中正常組14人，抑郁癥未用藥組11人，抑郁癥用藥組11人。這3組被試在年齡，性別與受教育程度上匹配。在被試者沒有刺激的并且清醒的狀態(tài)下，采集8分鐘的腦電信號。 本研究中深入研究三組被試在腦電信號的絕對功率，相對功率比，左右不對稱性和安靜過程差異性各個頻帶間的顯著差異，發(fā)現(xiàn)以下有意義的結(jié)果：絕對功率的alpha1，alpha2頻帶；左右不對稱性的theta頻帶；安靜過程差異性絕對功率方面的alpha2頻帶是素質(zhì)依賴性特征。絕對功率的delta頻帶；相對功率比的alpha1，theta頻帶；左右不對稱性的alpha1，alpha2，beta1，delta頻帶；安靜過程差異性方面的theta，delta頻帶是狀態(tài)依賴性特征。另外，絕對功率的beta1頻帶，相對功率比的beta1頻帶是可以反映抗抑郁劑副作用的特征。 本研究還使用抑郁癥腦電信號絕對功率，相對功率比，左右不對稱性，安靜過程差異性4個特征進行了分類研究。每種特征下又分為7個頻帶與全頻帶的綜合。分類算法分別是C4.5決策樹算法，樸素貝葉斯算法和k最近鄰算法。從3組分類結(jié)果來看，其中C4.5決策樹算法最高分類正確率達到70.00%，樸素貝葉斯算法最高能達到83.33%，k最近鄰算法最高能達到70.37%。并且對抑郁癥素質(zhì)依賴性和狀態(tài)依賴性特征做了分類檢驗。
[Abstract]:Depression is a high incidence of emotional disorders mental illness.The electroencephalogram (EEG) released by the human brain without stimulation can reflect the different states of the brain, including abnormal state. It is a common tool for the clinical examination of mental illness.First of all, this study hopes to find abnormal features with normal people in EEG signals of depression patients, so as to understand the disease mechanism of depression and to evaluate the diagnosis and treatment effect.Then three famous pattern recognition algorithms are used to study the classification of EEG signals using the features found in this study to test the classification effect of these features.In this study, 3 groups of subjects were collected from normal subjects, untreated depression patients and depressive patients.There were 14 cases in normal group, 11 cases in untreated depression group and 11 cases in depressive drug group.The three groups matched age, gender and educational attainment.When the subjects were not stimulated and awake, 8-minute EEG signals were collected.In this study, we studied the significant differences in the absolute power, relative power ratio, left and right asymmetry and quiet process difference between the three groups of subjects in different frequency bands, and found the following significant results: the absolute power of alpha1 / alpha2 band;The theta band with left and right asymmetry and the alpha2 band with absolute power difference in quiet process are quality-dependent.The delta frequency band of absolute power; the alpha1 / theta band of relative power ratio; the left and right asymmetrical alpha1 / alpha2B / beta / delta band; the deltas frequency band of the quiet process difference are state-dependent.In addition, the beta1 band of absolute power and the beta1 band of relative power ratio can reflect the side effects of antidepressants.In this study, four characteristics of depression EEG, such as absolute power, relative power ratio, left and right asymmetry and quiet process difference, were used to classify the EEG signals.Each feature is divided into seven bands and full band synthesis.The classification algorithms are C4.5 decision tree algorithm, naive Bayes algorithm and k-nearest neighbor algorithm.From the results of three groups of classification results, the C4.5 decision tree algorithm can achieve the highest classification accuracy of 70.00g and the naive Bayesian algorithm can reach the highest 83.33k nearest neighbor algorithm up to 70.37.At the same time, the quality dependence and state-dependent characteristics of depression were tested by classification.
【學(xué)位授予單位】：北京工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：R749.4

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