本文選題：腦電信號 + 統(tǒng)計復(fù)雜度�。� 參考：《南京郵電大學》2014年碩士論文

【摘要】：腦電信號的生理機制分析對于評估大腦機能的活躍程度以及健康狀態(tài)具有重要的意義。本文提出了兩種新的基于統(tǒng)計復(fù)雜度的腦電信號分析方法，期望能夠?qū)鹘y(tǒng)臨床腦電信號的病理分析起到一定的參考價值。本文的工作主要有以下兩點： 一、基于LMCD癲癇異常腦電信號統(tǒng)計復(fù)雜度分析 本文介紹了另一種基于歐幾里得空間距離定義的差異，應(yīng)用基于LMCD差異的統(tǒng)計復(fù)雜度算法對腦電信號進行分析研究。對癲癇發(fā)作時采集的異常腦電信號及正常腦電信號中提取的β波進行復(fù)雜度數(shù)值計算，結(jié)果是癲癇患者的異常腦電信號中β波的平均統(tǒng)計復(fù)雜度顯著高于正常人的，證明基于LMCD可以作為衡量腦電信號是否異常的參數(shù)。 二、基于統(tǒng)計復(fù)雜度算法的少中年腦電信號分析及對比 首先，，介紹了一種用于度量時間序列概率分布之間的差異（散度）：Jensen-ShannonDivergence（JSD）。本文介紹的JSD算法，在“非平衡項”的概念基礎(chǔ)上，從而得出了基于JSD統(tǒng)計復(fù)雜度的算法，并利用統(tǒng)計復(fù)雜度估計時間序列隨機本質(zhì)，該算法可以證明腦電信號在不同年紀段具有明顯差異。最后應(yīng)用這個對少中年腦電信號進行數(shù)值計算及對比，結(jié)果是中年人的統(tǒng)計復(fù)雜度顯著高于年少年，表明基于JSD算法的統(tǒng)計復(fù)雜度可以有效地區(qū)分這兩個年級段的腦電信號，并可以作為評估腦功能活躍程度的重要指標。 為了把我們研究的算法實現(xiàn)臨床使用，輔助醫(yī)生診斷癲癇疾病，我們在安卓系統(tǒng)中把上述算法進行了實現(xiàn)。
[Abstract]:The analysis of the physiological mechanism of EEG plays an important role in evaluating the activity of brain function and the state of health. In this paper, two new methods of EEG analysis based on statistical complexity are proposed, which are expected to play a certain reference value in the pathological analysis of traditional clinical EEG signals. The main work of this paper is as follows: 1. Statistical complexity analysis of abnormal EEG signals based on LMCD In this paper, another kind of Euclidean spatial distance definition is introduced. The statistical complexity algorithm based on Euclidean difference is used to analyze the EEG signal. The complexity of abnormal EEG signals collected during seizures and 尾 waves extracted from normal EEG signals were calculated. The results showed that the average statistical complexity of 尾 waves in abnormal EEG signals of epileptic patients was significantly higher than that in normal subjects. It is proved that LMCD can be used as a parameter to measure whether EEG signal is abnormal or not. Second, the analysis and comparison of EEG signals in middle and young age based on statistical complexity algorithm. Firstly, a new method to measure the difference between probability distributions of time series is introduced. The JSD algorithm introduced in this paper is based on the concept of "non-equilibrium term", and an algorithm based on the statistical complexity of JSD is obtained, and the stochastic nature of time series is estimated by statistical complexity. The algorithm can prove that there are obvious differences in EEG signals at different ages. Finally, this method is used to calculate and compare the EEG signals of young and middle-aged people. The results show that the statistical complexity of middle-aged people is significantly higher than that of teenagers, which indicates that the statistical complexity based on JSD algorithm can effectively distinguish the EEG signals between the two grades. It can be used as an important index to evaluate the degree of brain function activity. In order to realize the clinical use of the algorithm we studied to assist doctors in the diagnosis of epilepsy, we implemented the algorithm in Android.
【學位授予單位】：南京郵電大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：R741.044;TN911.6

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