【摘要】：腦卒中是一類高發(fā)病率,高致死率的疾病。在預(yù)測(cè)腦卒中發(fā)生以及在腦卒中患者康復(fù)觀察的過程中,并沒有很好的客觀評(píng)價(jià)方法,只能通過醫(yī)生的臨床經(jīng)驗(yàn)。所以本論文通過結(jié)合人體聲音產(chǎn)生的生理學(xué)特點(diǎn),利用非線性動(dòng)力學(xué)方法分析聲音時(shí)間序列,提取特征量分析腦卒中患者的大腦的損傷狀態(tài),嘗試可以找到度量腦卒中患者大腦狀態(tài)的特征量。為腦卒中患者康復(fù)及預(yù)防提供客觀評(píng)價(jià)。 本文對(duì)腦卒中患者的診斷判別方法進(jìn)行了分析和研究,并最終通過聲音診斷技術(shù)實(shí)現(xiàn)了腦卒中患者和健康人的分類。對(duì)聲音診斷技術(shù)的四個(gè)方面(即腦卒中病人聲音采集、腦卒中病人聲音信號(hào)分析處理、腦卒中患者診斷特征量的構(gòu)造和腦卒中患者分類)進(jìn)行研究探索,并取得了以下研究結(jié)果： 1)提出了通過聲音來研究大腦狀態(tài)的方法,并且從語音產(chǎn)生的神經(jīng)機(jī)制和腦成像機(jī)制的角度對(duì)利用聲音分析大腦狀態(tài)提供了理論上的支撐。并且結(jié)合腦卒中患者實(shí)際情況提出了最適合采集的音節(jié)。 2)提出了基于聲音時(shí)間序列的混沌特性用非線性動(dòng)力學(xué)的方法來分析腦卒中患者的大腦狀態(tài)的方法。對(duì)聲音非線性時(shí)間序列進(jìn)行相空間重構(gòu),分別用互信息法得到的時(shí)間延遲和用改進(jìn)偽最近鄰法得到的CAO方法得到的嵌入維數(shù)重構(gòu)相空間和吸引子。最后用小數(shù)據(jù)法計(jì)算聲音時(shí)間序列的最大Lyapunov指數(shù),提取得到的這些混沌特征量都證明了聲音時(shí)間序列具有混沌特性。 3)首次提出了利用改進(jìn)的替代數(shù)據(jù)法得到一種新的非線性特征量來反映聲音時(shí)間序列的非線性特征,進(jìn)而用于反映腦卒中患者的大腦狀態(tài)。該方法將替代數(shù)據(jù)法和關(guān)聯(lián)維數(shù)相結(jié)合得到了新的非線性特征量即歸一化方差檢測(cè)量。這一新的特征量反映了非線性聲音時(shí)間序列和聲音序列的替代數(shù)據(jù)(不具有混沌特性)在關(guān)聯(lián)維數(shù)上間的差異,比單純的非線性聲音時(shí)間序列的關(guān)聯(lián)維數(shù)更好得反映了腦卒中患者因?yàn)槟X損傷導(dǎo)致的變異聲音的非線性性質(zhì)。 4)對(duì)聲音特征量進(jìn)行模式分類。本文首先對(duì)所有聲音樣本提取聲音特征量,包括互信息圖的第一個(gè)最小值,關(guān)聯(lián)維數(shù),最大Lyapunov指數(shù)以及歸一化方差檢測(cè)量。對(duì)這些特征量按照健康人和腦卒中病人進(jìn)行統(tǒng)計(jì)對(duì)比分析。通過圖表的形式生動(dòng)直觀得看出兩類聲音信號(hào)的差異。然后再利用K近鄰分類方法對(duì)組合特征量進(jìn)行分類,分類結(jié)果表明了新的歸一化方差檢測(cè)量能夠提高分類準(zhǔn)確度,而提取出的非線性特征量可以用于對(duì)腦卒中患者和健康人進(jìn)行分類。這也為以后用聲音時(shí)間序列分析度量大腦狀態(tài)提供了研究方向和基礎(chǔ)。
[Abstract]:Stroke is a kind of disease with high incidence and high mortality. In predicting the occurrence of stroke and in the course of rehabilitation observation of stroke patients, there is no good objective evaluation method, only through the doctor's clinical experience. Therefore, combining the physiological characteristics of human voice production, this paper uses nonlinear dynamic method to analyze the sound time series, and extracts the characteristic quantity to analyze the brain damage state of stroke patients. Try to find characteristic quantities that measure the state of the brain in stroke patients. To provide objective evaluation for the rehabilitation and prevention of stroke patients. In this paper, the diagnosis and discrimination methods of stroke patients were analyzed and studied. Finally, the classification of stroke patients and healthy people was realized by sound diagnosis. In this paper, four aspects of sound diagnosis technology (namely, the sound acquisition of stroke patients, the analysis and processing of sound signals of stroke patients, the construction of diagnostic characteristics of stroke patients and the classification of stroke patients) were studied and explored. The following results are obtained: 1) A method to study the brain state through sound is proposed, and it provides theoretical support for the analysis of brain state by sound from the perspective of the neural mechanism of speech production and the brain imaging mechanism. According to the actual situation of stroke patients, the most suitable syllable is put forward. 2) A method of analyzing the brain state of stroke patients by nonlinear dynamics is proposed based on the chaotic characteristics of sound time series. The phase space reconstruction of sound nonlinear time series is carried out. The time delay obtained by mutual information method and the embedding dimension obtained by CAO method with improved pseudo-nearest neighbor method are used to reconstruct the phase space and attractor respectively. Finally, the maximum Lyapunov exponent of sound time series is calculated by small data method. The extracted chaotic features prove that the acoustic time series have chaotic properties. 3) A new nonlinear feature of sound time series is first proposed by using the improved alternative data method to reflect the nonlinear characteristics of the sound time series. In turn, it is used to reflect the brain state of stroke patients. This method combines the substitution data method and the correlation dimension to obtain a new nonlinear characteristic measure called normalized variance detection. This new feature reflects the difference between the correlation dimension of the nonlinear sound time series and the alternative data of the sound sequence (which does not have chaotic characteristics). The correlation dimension of the time series is better than the correlation dimension of the nonlinear acoustic time series, which reflects the nonlinear characteristics of the variant sound caused by brain injury in stroke patients. 4) the pattern classification of the sound characteristic quantity is carried out. In this paper, sound features are extracted from all sound samples, including the first minimum of mutual information graph, correlation dimension, maximum Lyapunov exponent and normalized variance detection. These characteristics were statistically compared with those of healthy people and stroke patients. The difference between the two types of sound signals can be seen vividly and intuitively through the form of a chart. Then the K-nearest neighbor classification method is used to classify the combined feature quantity. The classification results show that the new normalized variance detection method can improve the classification accuracy. The extracted nonlinear features can be used to classify stroke patients and healthy people. This also provides the research direction and foundation for the measurement of brain state by sound time series analysis.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：R743.3

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