單通道誘發(fā)電位信號的快速提取算法研究
發(fā)布時間：2019-06-11 02:40
【摘要】：誘發(fā)電位(EP)信號為神經(jīng)科學的理論研究與臨床應(yīng)用提供許多重要信息,它反映了相應(yīng)的感覺通路及大腦皮層區(qū)域的神經(jīng)電活動。深入地分析研究EP信號,對于研究大腦活動規(guī)律及其信息處理機制、發(fā)展神經(jīng)電生理學理論與應(yīng)用、臨床診斷評價神經(jīng)系統(tǒng)的功能以及臨床術(shù)中監(jiān)控等均具有重要意義。EP信號通常深深地湮沒在自發(fā)腦電(EEG)信號之中。因此,從強EEG背景噪聲中有效地提取出EP信號一直是生物醫(yī)學信號處理領(lǐng)域研究的重要問題之一。目前在臨床中廣泛應(yīng)用的相干平均法存在丟失EP信號細節(jié)以及因神經(jīng)系統(tǒng)疲勞而導致較大測量誤差等不足�；诖藛栴},對EP信號快速提取方法的研究成為近年來的一個研究熱點與難點。所謂快速提取主要相對于相干平均法而言,是指對EP信號的少次提取、單次提取以及動態(tài)跟蹤等。 本文主要研究在單通道條件下對EP信號的快速提取算法,研究成果可以歸納如下： (1)深入研究了基于稀疏表示模型的單通道EP信號少次提取問題,提出了基于混合訓練字典的稀疏表示方法與基于聯(lián)合稀疏表示的方法用于單通道EP信號的少次提取。針對已有混合字典稀疏表示中使用通用過完備字典造成的對信號成分的錯誤劃分問題,首先根據(jù)EP與EEG信號的不同特點提出了基于混合訓練字典的稀疏表示方法,通過使用其他少次觀測數(shù)據(jù)設(shè)計模板信號并訓練分別與EP和EEG信號相適應(yīng)的過完備字典,該方法有效地減少了使用混合字典稀疏表示過程中的錯分問題。然后提出基于聯(lián)合稀疏表示的EP信號少次提取方法,利用EP信號的準周期性,同時使用少次相鄰觀測信號進行聯(lián)合稀疏表示,可以在較低的信噪比情況下更有效地提取EP信號。 (2)深入研究了基于時間自相關(guān)函數(shù)的單通道EP信號單次提取問題,提出了兩種基于源信號時間自相關(guān)函數(shù)的波形估計方法并用于單通道EP信號的單次提取。利用時間自相關(guān)函數(shù)帶來的信息,首先提出一種以源信號時間自相關(guān)函數(shù)作約束的波形估計方法,它利用源信號的時間自相關(guān)函數(shù)構(gòu)造非線性方程組,并借助大規(guī)模方程組的數(shù)值解法,把從信噪比較低的觀測數(shù)據(jù)中直接估計源信號這一較困難的問題,轉(zhuǎn)化成分別估計迭代初始值與源信號時間自相關(guān)函數(shù)的問題。然后針對當源信號時間自相關(guān)函數(shù)估計精度較低時,該方法需要較多計算時間的不足,又提出了基于時間自相關(guān)函數(shù)最優(yōu)化的波形估計方法。該方法能夠在估計精度與計算速度之間較好地取得平衡,更加適用于對計算效率要求較高的應(yīng)用。將以上兩種方法應(yīng)用于單通道EP信號的單次提取問題,取得了較好的效果。 (3)深入研究了脈沖噪聲環(huán)境下基于徑向基函數(shù)神經(jīng)網(wǎng)絡(luò)模型的單通道EP信號自適應(yīng)估計問題,提出了三種韌性單通道EP信號自適應(yīng)估計方法。當臨床應(yīng)用中EP信號的背景噪聲呈現(xiàn)非高斯脈沖特性時,更加適合使用α穩(wěn)定分布來描述。針對基于最小平均p范數(shù)準則的EP信號自適應(yīng)估計方法無法在α值動態(tài)變化時較好工作的不足,首先提出了基于最小平均絕對偏差準則的韌性單通道EP信號自適應(yīng)估計方法,它可以在α值動態(tài)變化的情況下較好地工作。但該方法使用的二值化變換完全丟失了誤差信號的幅度信息,導致其無法在估計精度與收斂速度之間很好地平衡。針對該不足,又提出了基于非線性Sigmoid變換的韌性單通道EP信號自適應(yīng)估計方法,可以應(yīng)用于α值動態(tài)變化的情況,并可以較好地保留誤差信號的幅度信息,具有較好的估計精度與收斂速度。最后提出一種基于最大相關(guān)熵準則的韌性單通道EP信號自適應(yīng)估計方法,通過選取適當?shù)暮碎L參數(shù),它可以較好地工作在α值動態(tài)變化的情況下。將以上算法應(yīng)用于單通道EP信號的韌性自適應(yīng)估計,均取得較好的估計結(jié)果。
[Abstract]:The evoked potential (EP) signal provides a number of important information for the theoretical study and clinical application of neuroscience, which reflects the corresponding sensory pathway and the neural electrical activity in the cerebral cortex area. In-depth analysis of the EP signal is of great significance in the study of the law of brain activity and its information processing mechanism, the development of the neuroelectrophysiology theory and the application, the function of the clinical diagnosis and evaluation of the nervous system, and the monitoring of clinical operation. The EP signal is typically deeply annihilated in a spontaneous brain (EEG) signal. Therefore, the effective extraction of the EP signal from the strong EEG background noise has been one of the most important problems in the field of biomedical signal processing. At present, the coherent average method widely used in the clinical application has the defects of losing the detail of the EP signal and the large measurement error due to the fatigue of the nervous system. Based on this problem, the research of the rapid extraction method of the EP signal has become a hot point and difficulty in recent years. The so-called fast extraction is mainly relative to the coherent average method, which means less extraction, single extraction and dynamic tracking of the EP signal. In this paper, the fast extraction algorithm of the EP signal under the single channel condition is studied, and the research results can be summarized as follows: Next: (1) In-depth study of a single-channel EP signal based on sparse representation model In this paper, a sparse representation method based on a mixed training dictionary and a method based on the combined sparse representation are proposed to reduce the single channel EP signal. In order to solve the problem of misclassification of the signal component caused by the general overcomplete dictionary in the sparse representation of the existing mixed dictionary, the sparse table based on the mixed training dictionary is first proposed according to the different characteristics of the EP and the EEG signal. The method comprises the following steps of: designing a template signal by using other less observation data and training an over-complete dictionary corresponding to the EP and the EEG signal, the method effectively reduces the error in the process of using the mixed dictionary sparse representation, In this paper, the secondary extraction method of the EP signal based on the joint sparse representation is proposed, and the quasi-periodicity of the EP signal is used, and the combined sparse representation of the adjacent observation signals is used at the same time, and the E signal can be extracted more effectively in the case of lower signal-to-noise ratio. P signal. (2) In-depth study of single-channel EP signal extraction problem based on time-autocorrelation function, two waveform estimation methods based on time-autocorrelation function of source signal are proposed and used for single-channel EP signal A waveform estimation method based on the time autocorrelation function of the source signal is proposed, which uses the time self-correlation function of the source signal to construct a nonlinear system of linear equations, and by means of large-scale equations The numerical solution of the source signal is directly estimated from the observation data with low signal-to-noise ratio, which is converted into the estimated iteration initial value and the source signal time self-correlation, respectively. The problem of the function is solved. Then, when the estimation accuracy of the source signal time from the correlation function is low, the method needs to be less than the calculation time, and then the wave based on the time autocorrelation function is proposed. The method can obtain the balance between the estimation precision and the calculation speed, and is more suitable for the requirement of the calculation efficiency. The application of the above two methods to single-channel EP signal extraction has been achieved. In this paper, the self-adaptive estimation of single-channel EP signal based on radial basis function neural network model is studied in this paper. Three kinds of flexible single-channel EP signals are put forward. The adaptive estimation method is more suitable for use when the background noise of the EP signal exhibits non-Gaussian pulse characteristics. In this paper, an EP signal adaptive estimation method based on the minimum average p-norm criterion is not able to work well at the dynamic change of the peak value, and a flexible single-channel EP signal based on the minimum mean absolute deviation criterion is first proposed. An adaptive estimation method, which can be used to dynamically change the value of the value but the binary transformation used by the method completely loses the amplitude information of the error signal, so that the error signal can not be in the estimation precision and the convergence speed, In this paper, the self-adaptive estimation method of the flexible single-channel EP signal based on the non-linear sigmoid transformation is proposed, which can be applied to the dynamic change of the amplitude value, and the amplitude information of the error signal can be better preserved. In this paper, an adaptive estimation method of a single-channel EP signal based on the maximum correlation entropy criterion is proposed. In the case of state change, the above algorithm is applied to the adaptive estimation of the toughness of the single-channel EP signal.
【學位授予單位】：大連理工大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：R338;TN911.7
本文編號：2496944
[Abstract]:The evoked potential (EP) signal provides a number of important information for the theoretical study and clinical application of neuroscience, which reflects the corresponding sensory pathway and the neural electrical activity in the cerebral cortex area. In-depth analysis of the EP signal is of great significance in the study of the law of brain activity and its information processing mechanism, the development of the neuroelectrophysiology theory and the application, the function of the clinical diagnosis and evaluation of the nervous system, and the monitoring of clinical operation. The EP signal is typically deeply annihilated in a spontaneous brain (EEG) signal. Therefore, the effective extraction of the EP signal from the strong EEG background noise has been one of the most important problems in the field of biomedical signal processing. At present, the coherent average method widely used in the clinical application has the defects of losing the detail of the EP signal and the large measurement error due to the fatigue of the nervous system. Based on this problem, the research of the rapid extraction method of the EP signal has become a hot point and difficulty in recent years. The so-called fast extraction is mainly relative to the coherent average method, which means less extraction, single extraction and dynamic tracking of the EP signal. In this paper, the fast extraction algorithm of the EP signal under the single channel condition is studied, and the research results can be summarized as follows: Next: (1) In-depth study of a single-channel EP signal based on sparse representation model In this paper, a sparse representation method based on a mixed training dictionary and a method based on the combined sparse representation are proposed to reduce the single channel EP signal. In order to solve the problem of misclassification of the signal component caused by the general overcomplete dictionary in the sparse representation of the existing mixed dictionary, the sparse table based on the mixed training dictionary is first proposed according to the different characteristics of the EP and the EEG signal. The method comprises the following steps of: designing a template signal by using other less observation data and training an over-complete dictionary corresponding to the EP and the EEG signal, the method effectively reduces the error in the process of using the mixed dictionary sparse representation, In this paper, the secondary extraction method of the EP signal based on the joint sparse representation is proposed, and the quasi-periodicity of the EP signal is used, and the combined sparse representation of the adjacent observation signals is used at the same time, and the E signal can be extracted more effectively in the case of lower signal-to-noise ratio. P signal. (2) In-depth study of single-channel EP signal extraction problem based on time-autocorrelation function, two waveform estimation methods based on time-autocorrelation function of source signal are proposed and used for single-channel EP signal A waveform estimation method based on the time autocorrelation function of the source signal is proposed, which uses the time self-correlation function of the source signal to construct a nonlinear system of linear equations, and by means of large-scale equations The numerical solution of the source signal is directly estimated from the observation data with low signal-to-noise ratio, which is converted into the estimated iteration initial value and the source signal time self-correlation, respectively. The problem of the function is solved. Then, when the estimation accuracy of the source signal time from the correlation function is low, the method needs to be less than the calculation time, and then the wave based on the time autocorrelation function is proposed. The method can obtain the balance between the estimation precision and the calculation speed, and is more suitable for the requirement of the calculation efficiency. The application of the above two methods to single-channel EP signal extraction has been achieved. In this paper, the self-adaptive estimation of single-channel EP signal based on radial basis function neural network model is studied in this paper. Three kinds of flexible single-channel EP signals are put forward. The adaptive estimation method is more suitable for use when the background noise of the EP signal exhibits non-Gaussian pulse characteristics. In this paper, an EP signal adaptive estimation method based on the minimum average p-norm criterion is not able to work well at the dynamic change of the peak value, and a flexible single-channel EP signal based on the minimum mean absolute deviation criterion is first proposed. An adaptive estimation method, which can be used to dynamically change the value of the value but the binary transformation used by the method completely loses the amplitude information of the error signal, so that the error signal can not be in the estimation precision and the convergence speed, In this paper, the self-adaptive estimation method of the flexible single-channel EP signal based on the non-linear sigmoid transformation is proposed, which can be applied to the dynamic change of the amplitude value, and the amplitude information of the error signal can be better preserved. In this paper, an adaptive estimation method of a single-channel EP signal based on the maximum correlation entropy criterion is proposed. In the case of state change, the above algorithm is applied to the adaptive estimation of the toughness of the single-channel EP signal.
【學位授予單位】：大連理工大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：R338;TN911.7
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