基于希爾伯特—黃變換的高階聽(tīng)覺(jué)誘發(fā)電位提取研究
發(fā)布時(shí)間：2019-04-16 12:51
【摘要】：聽(tīng)覺(jué)誘發(fā)電位(Auditory evoked potentials, AEP)是聽(tīng)覺(jué)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。目前AEP已廣泛的應(yīng)用于評(píng)價(jià)嬰幼兒聽(tīng)力、鑒別診斷聽(tīng)神經(jīng)病變等方面。臨床上的AEP記錄主要采用刺激間間隔(Stimulus onset asynchrony, SOA)相等的低刺激率方案并通過(guò)總體平均方法提高信號(hào)的信噪比。當(dāng)刺激率過(guò)高,刺激間間隔小于誘發(fā)反應(yīng)的潛伏期時(shí),相鄰刺激所誘發(fā)的AEP波形就會(huì)出現(xiàn)首尾重疊的現(xiàn)象,這種AEP稱為高刺激率AEP (High stimulus rate AEP, HSR-AEP),其所包含的暫態(tài)AEP成分稱為高階AEP (High-order AEP, HO-AEP)。高刺激率條件下由于聽(tīng)神經(jīng)負(fù)荷加重從而有助于聽(tīng)覺(jué)系統(tǒng)適應(yīng)性的評(píng)估以及一些聽(tīng)覺(jué)系統(tǒng)疾病的機(jī)理研究和臨床診斷,并且HO-AEP有利于麻醉深度監(jiān)測(cè)和睡眠狀態(tài)評(píng)估�？紤]到在給予相同刺激個(gè)數(shù)的情況下,高刺激率記錄比常規(guī)記錄的時(shí)間要短許多,人們也期望高刺激率記錄可以減少記錄時(shí)間。因此HO-AEP的研究具有廣闊的臨床應(yīng)用前景。 然而,HSR-AEP的瞬時(shí)分量產(chǎn)生重疊的問(wèn)題是無(wú)法通過(guò)傳統(tǒng)的平均方法解決的,其重疊過(guò)程在工程學(xué)上可視為HO-AEP與刺激序列的循環(huán)卷積效應(yīng)所導(dǎo)致�；谶@一模型,人們對(duì)刺激序列中的各個(gè)SOA采用抖動(dòng)(Jitter)技術(shù),即應(yīng)用不規(guī)則的SOA代替相等的SOA,以便對(duì)HSR-AEP去卷積還原HO-AEP。一種抖動(dòng)較小的刺激序歹(?)——CLAD (Continuous loop averaging deconvolution)去卷積方案,被廣泛的應(yīng)用于重建HO-AEP。但是該方案去卷積過(guò)程是在頻域中進(jìn)行的,會(huì)對(duì)某些頻帶的噪聲進(jìn)行放大,從而影響高刺激率記錄的效率。如何在少次平均的情況下獲得較高質(zhì)量的去卷積前的信號(hào)是目前研究的熱點(diǎn),因此本文的主要研究工作包括： 1、運(yùn)用希爾伯特-黃變換(Hilbert-Huang transform, HHT)與非線性閾值濾波相結(jié)合方法提高信號(hào)的信噪比,減少平均次數(shù)。HHT包括經(jīng)驗(yàn)?zāi)B(tài)分解(Empirical mode decomposition, EMD)和Hilbert變換兩部分,其中EMD能夠根據(jù)腦電信號(hào)的局部特征尺度將其分解為一組頻率由高到低的固有模態(tài)函數(shù)(Intrinsic mode functions, IMFs),通過(guò)對(duì)每個(gè)IMF進(jìn)行Hilbert變換就能確定其頻率范圍。EMD相當(dāng)于一個(gè)帶通濾波過(guò)程,能夠?qū)⒉煌l率的信號(hào)和噪聲相對(duì)分離,但是它本身并不能區(qū)分混疊在同一頻率段的有用信號(hào)和噪聲,因此本文提出了對(duì)IMFs進(jìn)行進(jìn)一步的閾值濾波處理方法。因?yàn)楸疚乃信d趣的AEP的頻率范圍是20-100Hz,所以將包含主要AEP頻率成分的IMFs分為有用信號(hào)層,高頻的IMFs歸為噪聲層,低頻的包含較少信息的IMFs歸為趨勢(shì)層。由于相應(yīng)頻帶的IMFs的幅值分布近似于均值為零的高斯分布,因此本文根據(jù)其標(biāo)準(zhǔn)差和分類來(lái)決定相應(yīng)層的閾值。對(duì)噪聲層IMFs選取較小濾波閾值,盡可能的去除較多的噪聲,加強(qiáng)高頻信號(hào)的平穩(wěn)性。信號(hào)層包含了主要的AEP成分,但是仍然包含了大量的瞬態(tài)干擾,閾值濾波主要是去除對(duì)平均處理影響較大的大幅值的干擾。對(duì)于信號(hào)層本文提出了兩種濾波方法：整體濾波和區(qū)間濾波。整體濾波是當(dāng)某個(gè)IMF中出現(xiàn)大于所設(shè)定閾值的波幅時(shí)將整個(gè)IMF全部去除。區(qū)間濾波是將IMF中出現(xiàn)的大于所設(shè)定閾值的波峰去除,保留IMF的其余部分。 基于上述思想該部分設(shè)計(jì)了三種濾波方案：(1)直接提取信號(hào)層的IMFs重建信號(hào)；(2)對(duì)噪聲層采用類似軟閾值的處理方法,對(duì)信號(hào)層分別采取整體濾波和區(qū)間濾波的方法處理。最后將重建后的EEG信號(hào)分別做總體平均處理,得到估計(jì)的HSR-AEP。通過(guò)三個(gè)受試者的臨床數(shù)據(jù)對(duì)以上的濾波方法進(jìn)行驗(yàn)證,并計(jì)算信噪比作為評(píng)價(jià)標(biāo)準(zhǔn),結(jié)果表明上述的三種濾波方法都能夠有效的提高信號(hào)質(zhì)量,減少平均次數(shù)。 2、運(yùn)用HHT與總體相關(guān)技術(shù)(Ensemble correlation, EC)相結(jié)合的方法提取暫態(tài)AEP。在EMD分解時(shí),先將連續(xù)的EEG數(shù)據(jù)按一個(gè)周期刺激序列對(duì)應(yīng)時(shí)間分段,形成一組等長(zhǎng)的EEG數(shù)據(jù)段(稱為EEG掃程)。由于每個(gè)EEG掃程是由相同刺激序列所誘發(fā),故在背景噪聲的掩蓋下含有相同的AEP成分。經(jīng)過(guò)EMD分解后,各層IMF的信噪比不同。信噪比較高的IMF之間存在一定的相關(guān)性。因此不同EEG掃程經(jīng)EMD分解后IMF之間的EC函數(shù)可以作為一維濾波函數(shù),對(duì)IMF進(jìn)行加權(quán)濾波。為了強(qiáng)化這種相關(guān)性,我們?cè)贓MD分解前先對(duì)EEG掃程進(jìn)行適當(dāng)?shù)姆纸M平均,以提高EEG掃程的信噪比。與閾值法相比,采用EC濾波法處理的IMF信號(hào),無(wú)需人為對(duì)IMF進(jìn)行分類和確定閾值,減少了主觀因素的影響。通過(guò)對(duì)相同的數(shù)據(jù)集進(jìn)行檢驗(yàn),結(jié)果表明該方法能有效的抑制噪聲提高誘發(fā)信號(hào)的信噪比,并且不需要信號(hào)的先驗(yàn)知識(shí)和人為干預(yù),具有廣泛的應(yīng)用范圍。
[Abstract]:Auditory evoked potentials (AEP) are the bioelectrical changes in the central nervous system that are associated with external stimuli after the hearing system is stimulated by a particular sound. At present, AEP has been widely used in the evaluation of the hearing of infants and the differential diagnosis of auditory neuropathy. The clinical AEP records mainly employ a low stimulation rate scheme equal to the inter-stimulation interval (SOA) and improve the signal-to-noise ratio of the signal by the overall averaging method. When the stimulation rate is too high and the inter-stimulation interval is less than the latent period of the evoked response, the AEP waveform induced by the adjacent stimulation will have an end-to-end overlap, which is known as the high stimulation rate AEP (HSR-AEP), and the transient AEP component contained therein is referred to as a high-order AEP (High-order AEP, HO-AEP). It is helpful to evaluate the adaptability of auditory system and the mechanism and clinical diagnosis of some auditory system diseases under the condition of high stimulation rate, and HO-AEP is beneficial to the monitoring of anesthesia depth and the evaluation of sleep state. In view of the fact that the high stimulation rate recording is much shorter than the conventional recording time in the case of giving the same number of stimuli, it is also expected that the high stimulation rate recording can reduce the recording time. Therefore, the research of HO-AEP has a broad prospect of clinical application. However, the problem of the superposition of the instantaneous components of the HSR-AEP is not solved by the conventional averaging method, and the overlapping process can be considered in engineering as the cyclic convolution effect of the HO-AEP and the stimulation sequence. To this model, a Jitter technique is applied to each SOA in the stimulation sequence, i.e., an irregular SOA is applied in place of the same SOA, so that the HSR-AEP is deconvolved to restore the HO-AE P. A less jittery stimulus. (?) _ CLAD (Continuous loop) deconvolution scheme is widely used in the reconstruction of HO-AE P. However, the deconvolution process of the scheme is carried out in the frequency domain, and the noise of certain frequency bands can be amplified, thereby affecting the effect of high stimulation rate recording. The main research work package of this paper is how to obtain high-quality deconvolution before deconvolution is the hot spot of current research. The method of combining Hilbert-Huang transform (HHT) and non-linear threshold filtering to improve the signal signal-to-noise ratio The HHT consists of the empirical mode decomposition (EMD) and the Hilbert transform, in which the EMD can be decomposed into a set of intrinsic mode functions (IM) from high to low according to the local characteristic scale of the brain electrical signal. Fs), which can be determined by Hilbert transform for each IMF The EMD is equivalent to a band-pass filtering process, which can separate the signal and noise of different frequencies, but it does not distinguish the useful signal and noise of the aliasing in the same frequency band, so this paper puts forward a further threshold filter for IMFs. Since the frequency range of the AEP of interest is 20-100 Hz, the IMFs containing the main AEP frequency components are divided into a useful signal layer, the high-frequency IMFs are classified as a noise layer, and the low-frequency IMFs containing less information are classified into a useful signal layer. The trend layer. Since the amplitude distribution of the IMFs of the corresponding frequency band is similar to the Gaussian distribution with the mean value of zero, the corresponding layer is determined according to the standard deviation and the classification. And the lower filtering threshold is selected for the noise layer IMFs, more noise is removed as much as possible, and the high-frequency signal is enhanced. stationarity. The signal layer contains the primary AEP component, but still contains a large amount of transient interference, the threshold filtering is mainly to remove a significant value that has a greater impact on the average processing In this paper, two kinds of filtering methods are proposed in this paper: the whole filtering and the area Inter-filtering. The overall filtering is the whole of the IMF when the amplitude of a certain IMF is greater than the set threshold the interval filtering is to remove the peak of the IMF that is greater than the set threshold, and reserve the IMF the method comprises the following steps of: (1) directly extracting an IMFs reconstruction signal of a signal layer; (2) adopting a processing method similar to a soft threshold to the noise layer, and finally, the reconstructed EEG signals are respectively subjected to overall average processing to obtain an estimated HS, R-AEP. The above filtering method is validated by the clinical data of three subjects, and the signal-to-noise ratio is calculated as the evaluation standard. The results show that the above three filtering methods can effectively improve the signal quality and reduce the signal-to-noise ratio. the method of combining the HHT with the general correlation technique (EC) The transient AEP is extracted. At the time of EMD decomposition, the continuous EEG data is first segmented according to a periodic stimulation sequence to form a set of equal-length EEG data segments (said for EEG scanning). Since each EEG sweep is induced by the same stimulation sequence, the phase is contained under the masking of the background noise The same AEP composition. After the decomposition of EMD, each layer of IM The signal-to-noise ratio of F is different. Therefore, the EC function between the IMF and the IMF can be used as a one-dimensional filtering function, which can be used as a one-dimensional filtering function. F performs weighted filtering. In order to enhance this correlation, we have an appropriate packet averaging of the EEG sweep before the EMD is resolved to improve the EE The signal-to-noise ratio of the G sweep is compared with the threshold method. The IMF signal processed by the EC filtering method is not required to classify and determine the threshold value for the IMF, so that the signal-to-noise ratio is reduced. The results show that the method can effectively suppress the noise, improve the signal-to-noise ratio of the induced signal, and does not need the prior knowledge of the signal and the human intervention, and the method has the advantages that the method can effectively suppress the noise and improve the signal-to-noise ratio of the induced signal,
【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】：2011
【分類號(hào)】：R764
本文編號(hào)：2458791
[Abstract]:Auditory evoked potentials (AEP) are the bioelectrical changes in the central nervous system that are associated with external stimuli after the hearing system is stimulated by a particular sound. At present, AEP has been widely used in the evaluation of the hearing of infants and the differential diagnosis of auditory neuropathy. The clinical AEP records mainly employ a low stimulation rate scheme equal to the inter-stimulation interval (SOA) and improve the signal-to-noise ratio of the signal by the overall averaging method. When the stimulation rate is too high and the inter-stimulation interval is less than the latent period of the evoked response, the AEP waveform induced by the adjacent stimulation will have an end-to-end overlap, which is known as the high stimulation rate AEP (HSR-AEP), and the transient AEP component contained therein is referred to as a high-order AEP (High-order AEP, HO-AEP). It is helpful to evaluate the adaptability of auditory system and the mechanism and clinical diagnosis of some auditory system diseases under the condition of high stimulation rate, and HO-AEP is beneficial to the monitoring of anesthesia depth and the evaluation of sleep state. In view of the fact that the high stimulation rate recording is much shorter than the conventional recording time in the case of giving the same number of stimuli, it is also expected that the high stimulation rate recording can reduce the recording time. Therefore, the research of HO-AEP has a broad prospect of clinical application. However, the problem of the superposition of the instantaneous components of the HSR-AEP is not solved by the conventional averaging method, and the overlapping process can be considered in engineering as the cyclic convolution effect of the HO-AEP and the stimulation sequence. To this model, a Jitter technique is applied to each SOA in the stimulation sequence, i.e., an irregular SOA is applied in place of the same SOA, so that the HSR-AEP is deconvolved to restore the HO-AE P. A less jittery stimulus. (?) _ CLAD (Continuous loop) deconvolution scheme is widely used in the reconstruction of HO-AE P. However, the deconvolution process of the scheme is carried out in the frequency domain, and the noise of certain frequency bands can be amplified, thereby affecting the effect of high stimulation rate recording. The main research work package of this paper is how to obtain high-quality deconvolution before deconvolution is the hot spot of current research. The method of combining Hilbert-Huang transform (HHT) and non-linear threshold filtering to improve the signal signal-to-noise ratio The HHT consists of the empirical mode decomposition (EMD) and the Hilbert transform, in which the EMD can be decomposed into a set of intrinsic mode functions (IM) from high to low according to the local characteristic scale of the brain electrical signal. Fs), which can be determined by Hilbert transform for each IMF The EMD is equivalent to a band-pass filtering process, which can separate the signal and noise of different frequencies, but it does not distinguish the useful signal and noise of the aliasing in the same frequency band, so this paper puts forward a further threshold filter for IMFs. Since the frequency range of the AEP of interest is 20-100 Hz, the IMFs containing the main AEP frequency components are divided into a useful signal layer, the high-frequency IMFs are classified as a noise layer, and the low-frequency IMFs containing less information are classified into a useful signal layer. The trend layer. Since the amplitude distribution of the IMFs of the corresponding frequency band is similar to the Gaussian distribution with the mean value of zero, the corresponding layer is determined according to the standard deviation and the classification. And the lower filtering threshold is selected for the noise layer IMFs, more noise is removed as much as possible, and the high-frequency signal is enhanced. stationarity. The signal layer contains the primary AEP component, but still contains a large amount of transient interference, the threshold filtering is mainly to remove a significant value that has a greater impact on the average processing In this paper, two kinds of filtering methods are proposed in this paper: the whole filtering and the area Inter-filtering. The overall filtering is the whole of the IMF when the amplitude of a certain IMF is greater than the set threshold the interval filtering is to remove the peak of the IMF that is greater than the set threshold, and reserve the IMF the method comprises the following steps of: (1) directly extracting an IMFs reconstruction signal of a signal layer; (2) adopting a processing method similar to a soft threshold to the noise layer, and finally, the reconstructed EEG signals are respectively subjected to overall average processing to obtain an estimated HS, R-AEP. The above filtering method is validated by the clinical data of three subjects, and the signal-to-noise ratio is calculated as the evaluation standard. The results show that the above three filtering methods can effectively improve the signal quality and reduce the signal-to-noise ratio. the method of combining the HHT with the general correlation technique (EC) The transient AEP is extracted. At the time of EMD decomposition, the continuous EEG data is first segmented according to a periodic stimulation sequence to form a set of equal-length EEG data segments (said for EEG scanning). Since each EEG sweep is induced by the same stimulation sequence, the phase is contained under the masking of the background noise The same AEP composition. After the decomposition of EMD, each layer of IM The signal-to-noise ratio of F is different. Therefore, the EC function between the IMF and the IMF can be used as a one-dimensional filtering function, which can be used as a one-dimensional filtering function. F performs weighted filtering. In order to enhance this correlation, we have an appropriate packet averaging of the EEG sweep before the EMD is resolved to improve the EE The signal-to-noise ratio of the G sweep is compared with the threshold method. The IMF signal processed by the EC filtering method is not required to classify and determine the threshold value for the IMF, so that the signal-to-noise ratio is reduced. The results show that the method can effectively suppress the noise, improve the signal-to-noise ratio of the induced signal, and does not need the prior knowledge of the signal and the human intervention, and the method has the advantages that the method can effectively suppress the noise and improve the signal-to-noise ratio of the induced signal,
【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】：2011
【分類號(hào)】：R764
【參考文獻(xiàn)】
相關(guān)期刊論文 前2條
1 鐘佑明,秦樹(shù)人,湯寶平;希爾伯特黃變換中邊際譜的研究[J];系統(tǒng)工程與電子技術(shù);2004年09期
2 趙進(jìn)平,黃大吉;MIRROR EXTENDING AND CIRCULAR SPLINE FUNCTION FOR EMPIRICAL MODE DECOMPOSITION METHOD[J];Journal of Zhejiang University Science;2001年03期
，本文編號(hào)：2458791
本文鏈接：http://sikaile.net/yixuelunwen/yank/2458791.html
最近更新
教材專著
