【摘要】：聽覺系統(tǒng)受到特定的聲音刺激后中樞神經(jīng)系統(tǒng)產(chǎn)生的與外界刺激相關(guān)的生物電變化稱為聽覺誘發(fā)電位(Auditory Evoked Potentials, AEP),目前最為人熟知的是聽性腦干反應(yīng)(Auditory Brainstem Response, ABR),其中最常用的是短聲(click)刺激引出的ABR(click Evoked Brainstem Response, click-ABR),健康人的click-ABR一般包含7個(gè)特征波：Ⅰ～Ⅶ波,各波的潛伏期不同,因?yàn)樗鼈兌加懈髯缘钠鹪�。�?jiǎn)單聲(比如短聲、純音)誘發(fā)的ABR廣泛應(yīng)用于臨床聽閾估計(jì)、檢查聽覺通路中的病變、估計(jì)聽覺通路的完整性等。盡管如此,但由于這些簡(jiǎn)單聲與我們?nèi)粘Ｉ钏佑|到得復(fù)雜聲(如言語、環(huán)境噪聲等)差別甚大,它們引出的ABR能用來表征聽得見,但卻不能用來表述是否能聽懂。 人類環(huán)境中的復(fù)雜聲具有豐富的諧波、頻率信息變化迅速等特點(diǎn)。這些特性的在腦干中的編碼主要分為精確的時(shí)域編碼和頻譜編碼,引出的ABR包含瞬態(tài)反應(yīng)和持續(xù)反應(yīng)兩個(gè)部分。這些都是與刺激的聲學(xué)特性密切相關(guān)的,瞬態(tài)反應(yīng)與非周期特征有關(guān),而持續(xù)反應(yīng)則與刺激的周期性特征有關(guān)。盡管短聲和純音在各自引出的ABR中較好的表征了瞬態(tài)反應(yīng)和持續(xù)反應(yīng)模式,用它們卻無法估計(jì)出同時(shí)包含瞬態(tài)和持續(xù)特征的復(fù)雜聲引出的ABR。因此,相關(guān)學(xué)者開始研究復(fù)雜聲刺激引出的ABR。其中目前較為成熟的是合成言語(speech)單音節(jié)/da/誘發(fā)的ABR相關(guān)研究。 時(shí)間長(zhǎng)度為40ms的合成言語/da/誘發(fā)出的speech-ABR(da-ABR)研究已較為成熟。/da/包含瞬態(tài)和持續(xù)兩個(gè)部分,前者為輔音/d/,持續(xù)時(shí)間為10ms,后者為元音/a/,持續(xù)時(shí)間為30ms。元音/a/包含3個(gè)準(zhǔn)周期波：d、e、f波,周期約為10ms,這部分含有基頻(Fo)以及五個(gè)共振峰(F1-F5)的信息,其頻率分別：F0=103～121Hz,F1=220-720Hz,F2=1700～1240Hz,F3=2580-2500Hz,F4.5=3600-4500Hz。 /da/引出的言語誘發(fā)腦干反應(yīng)(Speech Evoked ABR, speech-ABR)可用來研究刺激的聲學(xué)特征是如何在聽覺系統(tǒng)中被編碼的,并且其結(jié)構(gòu)與/da/的聲學(xué)結(jié)構(gòu)非常相似,也可分為瞬態(tài)和持續(xù)兩個(gè)部分。前者包括起始反應(yīng)(onset reponse,OR),后者包括頻率跟隨反應(yīng)(frequency following response, FFR)。其中OR(V、A波)為輔音/d/引出的反應(yīng),FFR部分(D、E、F波)被認(rèn)為是刺激中的周期部分元音/a/引出的反應(yīng)。FFR部分的D、E、F波為準(zhǔn)周期波,分別為/a/中對(duì)應(yīng)準(zhǔn)周期波d、e、f引出,繼承了/a/的周期特性,周期為10ms左右,與/a/的周期基本相同。有研究表明FFR部分中的三個(gè)準(zhǔn)周期波具有顯著相關(guān)性,提示可將FFR部分視為一個(gè)整體性的生理指標(biāo)并可能源自相同的神經(jīng)處理機(jī)制。FFR部分時(shí)域波形的周期性表征了腦干是如何編碼元音/a/的基頻信息,而基頻信息是辨識(shí)言語的最重要信息,因此,可根據(jù)在speech-ABR的時(shí)域波形D、E、F波的完整性來判斷腦干對(duì)言語/a/編碼的程度。而在實(shí)際應(yīng)用中,雖然所有受試者的總體平均波形FFR都非常清晰,三個(gè)準(zhǔn)周期波容易分辨,但對(duì)單個(gè)受試者的speech-ABR進(jìn)行分析時(shí),FFR部分一般都很模糊,難以直觀分辨出三個(gè)準(zhǔn)周期波,這就說明需要采用信號(hào)處理技術(shù)在時(shí)域中突出FFR部分的準(zhǔn)周期波,有利于臨床應(yīng)用FFR部分來判斷腦干是否對(duì)言語/a/進(jìn)行了編碼或者編碼完整。 由于聽覺系統(tǒng)的非線性特點(diǎn),speech-ABR是非線性、非平穩(wěn)信號(hào),而希爾伯特黃變換(Hilbert-Huang Transform, HHT)是現(xiàn)在發(fā)展較成熟的處理非線性、分平穩(wěn)信號(hào)的方法。HHT分為兩個(gè)步驟：經(jīng)驗(yàn)?zāi)B(tài)分解(Empirical Mode Decomposition, EMD)和希爾伯特(Hilbert)變換。EMD是將非平穩(wěn)數(shù)據(jù)分解為有限個(gè)本征模態(tài)函數(shù)(ntrinsic Mode Function, IMF),每一層IMF基本都是平穩(wěn)信號(hào),并且IMF在任意時(shí)間點(diǎn)的振動(dòng)模式唯一,即瞬時(shí)頻率唯一。EMD是按照信號(hào)本身時(shí)間局部特征分解的,是自適應(yīng)的。speech-ABR原始時(shí)域波形中的任意時(shí)間點(diǎn)可能包含多個(gè)瞬時(shí)頻率,因?yàn)镕FR部分的三個(gè)準(zhǔn)周期波表征了元音/a/的基頻信息,則它們的振動(dòng)模式應(yīng)該相似,經(jīng)EMD后,它們應(yīng)該會(huì)集中分布在同一層或相同幾層IMF上。 本文提出一種基于希爾伯特黃變換的瞬時(shí)能量譜方法來分析speech-ABR中的頻率跟隨反應(yīng)部分,因?yàn)閟peech-ABR忠實(shí)的模擬了刺激信號(hào)的波形結(jié)構(gòu),再加上基于希爾伯特包絡(luò)的瞬時(shí)能量能夠突出極值,所以瞬時(shí)能量譜可能會(huì)比直接在原始時(shí)域波形中根據(jù)幅值絕對(duì)值最大來檢測(cè)FFR部分跟有效。本文用實(shí)驗(yàn)證明此方法可以將FFR部分從speech-ABR中突顯出來,有利于進(jìn)一步的分析和臨床應(yīng)用。該方法的具體步驟如下： (1)將個(gè)體speech-ABR進(jìn)行經(jīng)驗(yàn)?zāi)B(tài)(empirical mode decomposition, EMD)分解,得到有限個(gè)本征模態(tài)函數(shù)(intrinsic mode function, IMF); (2)計(jì)算每一層IMF的瞬時(shí)能量譜； (3)觀察個(gè)體speech-ABR分解后IMFs的瞬時(shí)能量譜,記錄準(zhǔn)周期波D、E、F出現(xiàn)的層數(shù)； (4)利用時(shí)窗為10ms,對(duì)該層瞬時(shí)能量譜17-47ms范圍內(nèi)尋找D、E、F波,記錄下潛伏期和極性。 實(shí)驗(yàn)記錄了29例成人的speech-ABR。實(shí)驗(yàn)過程包括四個(gè)步驟。首先,臨床專家根據(jù)V波是否明顯找出有效數(shù)據(jù),總共有25例(86%)。第二,臨床專家在有效數(shù)據(jù)中根據(jù)FFR部分是否明顯,將其分為兩組：典型組和非典型組。其中典型組有7例(28%),非典型組有18例(72%),非典型組的個(gè)體speech-ABR中FFR部分不明顯。第三,對(duì)著25例有效數(shù)據(jù)采用基于HHT的瞬時(shí)能量譜方法進(jìn)行處理。最后,臨床專家在這25例有效數(shù)據(jù)的瞬時(shí)能量譜中觀察FFR部分,發(fā)現(xiàn)有23例(92%)FFR明顯,有2(8%)例無法判斷。 結(jié)果表明,本文提出的方法突出表征了個(gè)體speech-ABR中的FFR部分,對(duì)檢測(cè)FFR部分確實(shí)有效。該方法與常規(guī)方法相比有如下兩個(gè)突出優(yōu)點(diǎn)：(1)個(gè)體speech-ABR中的FFR部分在瞬時(shí)能量譜中明顯比在speech-ABR時(shí)域波形中清晰可辨；(2)個(gè)體speech-ABR中FFR部分的準(zhǔn)周期波極性不盡相同,可為正波可為負(fù)波,但在瞬時(shí)能量譜中,均為正波�？梢�,此方法比常規(guī)方法更有效更準(zhǔn)確,可用于臨床上直接或輔助檢測(cè)D、E、F波。
[Abstract]:Auditory Evoked Potentials (AEP) are the bioelectrical changes in the central nervous system associated with external stimuli produced by specific sound stimulation of the auditory system. At present, the most well-known is Auditory Brainstem Response (ABR), the most common of which is the click-induced ABR (ABR). Click Evoked Brainstem Response (click-ABR), click-ABR in healthy people generally contains seven characteristic waves: _~_waves, the latency of each wave is different, because they have their own origins. Simple sound (such as short sound, pure tone) induced ABR is widely used in clinical auditory threshold estimation, examining the pathological changes in auditory pathways, estimating the integrity of auditory pathways. Nevertheless, because these simple sounds are so different from the complex sounds we are exposed to in our daily life (such as speech, ambient noise, etc.), their ABR can be used to indicate hearing, but it can not be used to express whether we can understand.
Complex sound in human environment has the characteristics of abundant harmonics and rapid change of frequency information.The encoding of these characteristics in the brain stem is mainly divided into precise time domain encoding and spectrum encoding.The ABR derived from ABR consists of transient response and continuous response.These are closely related to the acoustic characteristics of stimuli.Transient response and non-cyclic response are closely related. Although the short and pure tones represent the transient and persistent response patterns better in their respective ABRs, it is impossible to estimate the ABR induced by complex sounds with both transient and persistent characteristics. The most mature ABR. is the ABR related study of synthetic speech (speech) monosyllabic /da/.
The study of speech-ABR (d a-ABR) induced by 40 ms synthetic speech/d a/contains two parts: transient and persistent, the former being consonant/d/, lasting 10 ms, the latter being vowel/a/, lasting 30 ms. The frequencies of the five formants (F1-F5) are: F0 = 103-121Hz, F1 = 220-720Hz, F2 = 1700-1240Hz, F3 = 2580-2500Hz, F4.5 = 3600-4500Hz.
Speech Evoked ABR (speech-ABR) can be used to study how the acoustic characteristics of stimuli are encoded in the auditory system, and its structure is very similar to the acoustic structure of / DA / and can be divided into transient and persistent parts. Following response (FFR). In which OR (V, A) is a consonant / D / induced response, FFR (D, E, F) is considered to be a periodic part of the vowel / A / induced response in stimulation. In FFR, D, E, F waves are quasi-periodic waves, which are corresponding to quasi-periodic waves d, e, f in / A / respectively, inheriting the periodic characteristics of / A / with a period of about 10 ms. The periodicity of FFR partial time domain waveforms indicates how the brainstem encodes the fundamental frequency information of vowel/a/and the fundamental frequency information. Interest is the most important information for speech recognition, so the degree of speech/a/coding in the brain stem can be judged by the integrity of the D, E and F waves in the speech-ABR time domain. It is difficult to distinguish three quasi-periodic waves intuitively because of the ambiguity of the FFR part. This indicates that signal processing technology should be used to highlight the quasi-periodic wave of the FFR part in time domain, which is helpful for clinical application of the FFR part to judge whether the speech/a/is encoded or encoded intact by the brain stem.
Because of the nonlinearity of auditory system, speech-ABR is a non-linear and non-stationary signal. Hilbert-Huang Transform (HHT) is a well-developed method to deal with non-linear and stationary signals. HHT is divided into two steps: Empirical Mode Decomposition (EMD) and Hilbert (Hilbert). EMD decomposes non-stationary data into a finite number of intrinsic mode functions (IMFs). Each IMF layer is basically a stationary signal, and the IMF oscillation mode at any point in time is unique, that is, the instantaneous frequency is unique. EMD is decomposed according to the local time characteristics of the signal itself, and is adaptive. Speech-ABR original time domain wave. Any point of time in the form may contain multiple instantaneous frequencies, because the three quasi-periodic waves in the FFR part represent the fundamental frequency information of vowels/a/, then their vibration modes should be similar. After EMD, they should be concentrated on the same layer or the same IMF.
An instantaneous energy spectrum method based on Hilbert-Huang transform is proposed to analyze the frequency-following response in speech-ABR. Because speech-ABR faithfully simulates the waveform structure of the stimulus signal and the instantaneous energy based on Hilbert envelope can highlight the extreme value, the instantaneous energy spectrum may be more direct than the original time. In this paper, the experimental results show that this method can highlight the FFR part from speech-ABR, which is conducive to further analysis and clinical application.
(1) The individual speech-ABR is decomposed into empirical mode decomposition (EMD) and a finite number of intrinsic mode functions (IMF) are obtained.
(2) calculate the instantaneous energy spectrum of each layer of IMF.
(3) observe the instantaneous energy spectrum of IMFs after speech-ABR decomposition, and record the number of layers of D, E and F.
(4) The latency and polarity of D, E and F waves in the 17-47 MS instantaneous energy spectrum of the layer are recorded by using a time window of 10 ms.
Twenty-nine adults were recorded for speech-ABR. The procedure consisted of four steps. Firstly, clinical experts identified valid data based on whether the V wave was evident in 25 patients (86%). Secondly, according to whether the FFR part was evident in the valid data, clinical experts divided them into two groups: the typical group (7 cases) and the atypical group (28%). Thirdly, the instantaneous energy spectrum based on HHT was used to process 25 valid data. Finally, clinical experts observed the FFR in the instantaneous energy spectrum of 25 valid data, and found that 23 cases (92%) had significant FFR and 2 cases (8%) could not judge.
The results show that the proposed method highlights the FFR part of the individual speech-ABR and is effective for detecting the FFR part. Compared with the conventional method, this method has two outstanding advantages: (1) the FFR part of the individual speech-ABR is clearly distinguished in the instantaneous energy spectrum than in the speech-ABR time domain waveform; (2) the individual speech-ABR part is clearly distinguished in the instantaneous energy spectrum; (2) the individual speech-ABR part is distinguished in the speech-ABR time domain waveform. The polarity of the quasi-periodic wave in the FFR part is different from that in the FFR part, it can be positive wave, but it is positive wave in the instantaneous energy spectrum.
【學(xué)位授予單位】：南方醫(yī)科大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：R318.0

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