基于神經(jīng)元群模型的癲癇分析與控制
發(fā)布時(shí)間：2019-07-01 10:08
【摘要】：癲癇是一種常見(jiàn)的腦部疾病,具有反復(fù)性、突發(fā)性等特點(diǎn)。癲癇發(fā)作是由神經(jīng)元的高度興奮和高度同步化放電引起的,在腦電圖中表現(xiàn)為高幅的不規(guī)則過(guò)度放電。因此對(duì)癲癇發(fā)病機(jī)制和治療的研究一直是神經(jīng)病學(xué)的重大難題和研究熱點(diǎn)。而集總系數(shù)的神經(jīng)元群模型可以產(chǎn)生與實(shí)測(cè)腦電信號(hào)類似的波形,而且其模型參數(shù)都具有一定的生理學(xué)意義,這就在信號(hào)處理方法與神經(jīng)生理學(xué)研究成果之間搭建起了一座橋梁。這也為我們研究癲癇發(fā)作的生理學(xué)機(jī)制及其控制方法提供了平臺(tái)。在對(duì)神經(jīng)元群模型進(jìn)行改進(jìn)的基礎(chǔ)上,圍繞癲癇發(fā)作期和發(fā)作間歇期的生理學(xué)特征分析,癲癇興奮性、同步性的控制方案等內(nèi)容展開(kāi)以下研究：1)為了分析潛在于腦電信號(hào)下的神經(jīng)生理學(xué)機(jī)制,分別將腦電信號(hào)看作單個(gè)神經(jīng)元群和多個(gè)神經(jīng)元群的輸出,進(jìn)而識(shí)別癲癇發(fā)作不同時(shí)期腦電信號(hào)所對(duì)應(yīng)的模型參數(shù),通過(guò)比較模型參數(shù)的分布情況來(lái)討論癲癇發(fā)作不同時(shí)期神經(jīng)生理學(xué)機(jī)制的差異。首先,將腦電信號(hào)看作單神經(jīng)元群(Wendling)模型的輸出,為了仿真腦電信號(hào)測(cè)量中測(cè)量時(shí)間和設(shè)備增益對(duì)腦電信號(hào)波形的影響,在Wendling模型的基礎(chǔ)上引入了延遲單元和增益單元。改進(jìn)的Wendling模型參數(shù)的確定可以看作一個(gè)最優(yōu)化問(wèn)題,采用遺傳算法來(lái)求解最優(yōu)的模型參數(shù)組合使仿真腦電信號(hào)和實(shí)測(cè)腦電信號(hào)之間的誤差最小。采用本文提出的方法確定了癲癇發(fā)作期和發(fā)作間歇期不同腦電信號(hào)所對(duì)應(yīng)的模型參數(shù)。實(shí)驗(yàn)結(jié)果顯示改進(jìn)的Wendling模型可以較好地模擬實(shí)測(cè)的EEG信號(hào),并且基于遺傳算法的模型參數(shù)確定方法具有一定的穩(wěn)定性。對(duì)發(fā)作期和發(fā)作間歇期腦電信號(hào)所對(duì)應(yīng)的模型參數(shù)進(jìn)行了比較,并討論了癲癇發(fā)作不同時(shí)期神經(jīng)元興奮性和抑制性的差異。將腦電信號(hào)都看作一個(gè)神經(jīng)元群模型的輸出,而且這個(gè)神經(jīng)元群中的神經(jīng)元都具有統(tǒng)一的神經(jīng)生理學(xué)參數(shù)是不合理的,會(huì)因?yàn)楹雎粤松窠?jīng)元之間的差異性而導(dǎo)致模型輸出信號(hào)成分簡(jiǎn)單。為了更好地?cái)M合實(shí)測(cè)的腦電信號(hào),提出了并行連接的多神經(jīng)元群模型。多神經(jīng)元群模型包括多個(gè)神經(jīng)元群,模型輸出為各神經(jīng)元群輸出的線性組合。將實(shí)測(cè)的腦電信號(hào)看作多神經(jīng)元群模型的輸出,該模型中神經(jīng)元群數(shù)并不固定,而是在保證波形足夠匹配的前提下,使神經(jīng)元群數(shù)最小。上述問(wèn)題可以簡(jiǎn)化為一個(gè)有約束的lo范數(shù)最小化問(wèn)題,采用正交匹配追蹤方法來(lái)解該問(wèn)題。實(shí)驗(yàn)結(jié)果表明,發(fā)作期的神經(jīng)元群數(shù)明顯少于發(fā)作間歇期,而主要神經(jīng)元群的強(qiáng)度則較發(fā)作間歇期有大幅提升。這說(shuō)明在發(fā)作過(guò)程中,會(huì)有神經(jīng)元群融合的過(guò)程出現(xiàn),大量相似的神經(jīng)元聚集在一個(gè)神經(jīng)元群導(dǎo)致高幅度的發(fā)放。另外,從實(shí)驗(yàn)結(jié)果可以看出,發(fā)作期和發(fā)作間歇期腦電數(shù)據(jù)的興奮強(qiáng)度、抑制強(qiáng)度分布并無(wú)很大差別,但是發(fā)作期的興奮／抑制比有一定程度的升高。2)大腦神經(jīng)元的過(guò)度興奮一直被看作引發(fā)癲癇發(fā)作的主要原因,當(dāng)神經(jīng)系統(tǒng)的自身調(diào)節(jié)能力不足以維持興奮性-抑制性平衡的時(shí)候,就會(huì)引發(fā)癲癇發(fā)作。為了控制神經(jīng)元過(guò)度興奮引發(fā)的癲癇發(fā)作,制定了兩種策略。其一為降低過(guò)度興奮神經(jīng)元的興奮性,其二為增加抑制性以彌補(bǔ)神經(jīng)系統(tǒng)自身調(diào)節(jié)的不足。提出了癇性指數(shù)來(lái)描述癲癇發(fā)作程度,并用作PID控制器的被控參數(shù)來(lái)對(duì)癲癇發(fā)作進(jìn)行控制。以神經(jīng)元群模型為平臺(tái)仿真了興奮性增加導(dǎo)致的癲癇發(fā)作程度(癇性指數(shù))變化,進(jìn)而對(duì)兩種興奮性控制策略進(jìn)行了仿真。實(shí)驗(yàn)結(jié)果表明興奮強(qiáng)度增加而保持抑制強(qiáng)度不變會(huì)導(dǎo)致癇性指數(shù)的大幅度增加,導(dǎo)致癲癇發(fā)作。而用PID控制器分別降低興奮強(qiáng)度或增加抑制強(qiáng)度都可以維持興奮-抑制平衡,并緩解癲癇的發(fā)作。3)癲癇發(fā)作總是伴有多個(gè)神經(jīng)元群的超同步放電,這在腦電信號(hào)中表現(xiàn)為高幅的不規(guī)則過(guò)度放電。為了研究癲癇發(fā)作的超同步放電機(jī)制,并根據(jù)同步性進(jìn)行癲癇發(fā)作控制,構(gòu)建了包括多個(gè)具有串聯(lián)關(guān)系神經(jīng)元群的模型。由于同步性可以傳遞,討論兩個(gè)點(diǎn)的同步性,并不能反映整個(gè)區(qū)域的耦合結(jié)構(gòu)。采用PCA算法,引入了同步族的概念,并給出了同步族強(qiáng)度和參與率的計(jì)算方法。在神經(jīng)元群同步族的概念的基礎(chǔ)上,提出了不進(jìn)行癲癇病灶識(shí)別,而是針對(duì)癲癇發(fā)作所波及的同步族進(jìn)行統(tǒng)一控制的癲癇同步性控制方案,打破同步族中每一對(duì)神經(jīng)元群之間的耦合,從而削弱神經(jīng)元群之間的同步性。對(duì)一致耦合、不一致耦合的單同步族和兩同步族系統(tǒng)進(jìn)行了實(shí)驗(yàn),實(shí)驗(yàn)結(jié)果顯示,基于PCA的方法可以很好地識(shí)別存在的同步族及每個(gè)同步族中的神經(jīng)元群,針對(duì)同步族的控制策略也可以很好地控制癲癇發(fā)作。4)致癇區(qū)的準(zhǔn)確定位是保證癲癇治療并減少副作用的首要任務(wù)。在很多情況下,傳統(tǒng)視覺(jué)定位方法的效果并不能讓人滿意。信號(hào)處理的方法可以提供大量的信息來(lái)補(bǔ)償腦電信號(hào)視覺(jué)診斷的不足。致癇區(qū)定位被看作驅(qū)動(dòng)方識(shí)別問(wèn)題,提出了一種新的非線性互依賴性測(cè)度——加權(quán)排位互依賴性,作為驅(qū)動(dòng)方的指示符,因?yàn)樗梢詮哪X電信號(hào)提取耦合信息,特別是耦合方向信息。然后,PID控制器被用來(lái)進(jìn)行癲癇發(fā)作控制。癲癇發(fā)作控制方法首先需要采用加權(quán)排位互依賴性來(lái)識(shí)別致癇區(qū)。兩個(gè)單向連接在一起的神經(jīng)元群模型被用來(lái)進(jìn)行仿真所提出的控制方案。根據(jù)應(yīng)用的不同,可以通過(guò)兩個(gè)參數(shù)來(lái)調(diào)節(jié)加權(quán)排位互依賴性的靈敏度,它們各自的影響分別進(jìn)行了討論。仿真結(jié)果顯示采用加權(quán)排位互依賴性來(lái)進(jìn)行致癇區(qū)識(shí)別可以適應(yīng)于不同類型的致癇區(qū),可以對(duì)不同的致癇區(qū)取得98.84%的識(shí)別率。仿真同樣顯示PID控制器可以很好地控制神經(jīng)元群之間的同步性。神經(jīng)元群模型可以作為臨床實(shí)驗(yàn)的有效補(bǔ)充來(lái)進(jìn)行神經(jīng)生理學(xué)方面的研究,具有成本低、參數(shù)調(diào)整靈活等優(yōu)點(diǎn),本文的研究有助于推動(dòng)神經(jīng)元群模型在更多領(lǐng)域的應(yīng)用。而基于神經(jīng)元群模型的癲癇興奮性、同步性控制、病灶識(shí)別仿真可以為設(shè)計(jì)外部發(fā)作控制設(shè)備提供理論基礎(chǔ),可以進(jìn)一步應(yīng)用于臨床中。
[Abstract]:Epilepsy is a common disease of the brain, which has the characteristics of renaturation, bursty and so on. The seizure is caused by the highly excited and highly synchronized discharges of the neurons, which are characterized by an irregular over-discharge of the high amplitude in the electroencephalogram. Therefore, the study of the pathogenesis and treatment of epilepsy has been a major problem and research focus of neurology. The neuron group model with the total coefficient can produce a waveform similar to that of the measured brain electrical signal, and the model parameters have a certain physiological significance, and a bridge is set up between the signal processing method and the research result of the neurophysiology. This also provides a platform for our study of the physiological mechanism of the seizure and its control. On the basis of the improvement of the neuron group model, the following studies are carried out on the physiological characteristics, the excitability and the synchronicity of the epileptic seizure and the control scheme of the synchronicity:1) In order to analyze the neurophysiological mechanism underlying the brain electrical signal, The brain electrical signal is considered as the output of a single neuron group and a plurality of neuron groups, and then the model parameters corresponding to the brain electrical signals in different periods of the epileptic seizure are identified, and the difference of the neurophysiological mechanism during the different period of the epileptic seizure is discussed by comparing the distribution of the model parameters. First, the brain electrical signal is considered as the output of a single neuron group (Wendling) model, and the delay unit and the gain unit are introduced on the basis of the Wendling model in order to simulate the influence of the measurement time and the device gain on the waveform of the brain electrical signal in the measurement of the brain electrical signal. The determination of the improved Wendling model parameter can be considered as an optimization problem, and the genetic algorithm is used to solve the optimal model parameter combination to minimize the error between the simulated brain electrical signal and the measured brain electrical signal. The model parameters corresponding to the different brain electrical signals during the onset of the seizure and the onset of the attack were determined by the method presented in this paper. The experimental results show that the modified Wendling model can well simulate the measured EEG signal, and the model parameter determination method based on the genetic algorithm has certain stability. The model parameters corresponding to the brain electrical signals during the onset and the attack period were compared, and the differences of the excitability and inhibition of the neurons during the different period of the seizure were discussed. The brain electrical signal is regarded as the output of a neuron group model, and the neuron in the neuron group has a uniform neurophysiological parameter, which can cause the model output signal component to be simple because the difference between the neurons is ignored. In order to better fit the measured brain electrical signal, a multi-neuron group model for parallel connection is proposed. The multi-neuron group model comprises a plurality of neuron groups, and the model output is a linear combination of the output of each neuron group. The measured brain electrical signal is considered as the output of the multi-neuron group model, and the number of the neurons in the model is not fixed, but the number of the neurons is minimized on the premise of ensuring that the waveform is sufficiently matched. The above problems can be simplified into a constrained lo-norm minimization problem, and the problem is solved by using the orthogonal matching tracking method. The experimental results showed that the number of the neurons in the attack period was significantly less than that of the attack, while the intensity of the main neuron group was significantly higher than that in the intermittent period. This indicates that in the course of the attack, there will be a process of fusion of the neurons, and a large number of similar neurons accumulate in a neuron group leading to high amplitude distribution. In addition, it can be seen from the experimental results that the intensity of the excitation and the intensity distribution of the EEG data during the onset and the onset of the episode are not very different, But the excitement/ suppression ratio of the onset period is somewhat increased.2) The excessive excitement of the nervous system has been seen as the main cause of the onset of the seizure, and when the self-regulation capacity of the nervous system is not sufficient to maintain the excitability-inhibitory balance, the seizure can be initiated. In order to control the seizure caused by over-excitation of the neurons, two strategies were developed. One is to reduce the excitability of the over-excited neurons, and the other is to increase the inhibition to compensate for the insufficiency of the nervous system's own regulation. The epileptic seizure degree was described by the sex index, and the controlled parameters of the PID controller were used to control the seizure. The changes of the seizure degree (seizure index) caused by the increase of excitability were simulated with the neuron group model, and the two kinds of excitability control strategies were simulated. The results show that the increase of the excitation intensity and the retention of the inhibitory intensity can lead to a significant increase in the epileptic index, resulting in a seizure. Using the PID controller to reduce the excitation intensity or the increase of the inhibition intensity, the excitation-suppression balance can be maintained, and the onset of the seizure is relieved.3) The seizure is always accompanied by a super-synchronous discharge of a plurality of neuron groups, which is represented as an irregular over-discharge of the high amplitude in the brain electrical signal. In order to study the hypersynchronous discharge mechanism of the seizure, and to control the seizure according to the synchronicity, a model including a plurality of neuron groups with a series relationship was constructed. The synchronicity of the two points is discussed and the coupling structure of the whole area cannot be reflected because the synchronism can be transmitted. By adopting the PCA algorithm, the concept of the synchronous family is introduced, and the calculation method of the synchronous family intensity and the participation rate is given. on the basis of the concept of the group of neuron groups, the invention provides an epileptic synchronization control scheme which does not carry out the identification of the epileptic focus, but also carries out a unified control on the synchronous family affected by the seizure, and breaks the coupling between each pair of neuron groups in the synchronous family, Thereby weakening the synchronicity between the neuronal populations. The results show that the PCA-based method can well identify the existing synchronous family and the neuron group in each of the synchronous families. The control strategy for the synchronous family can also control the seizure.4) The accurate location of the epilepsy area is the primary task of ensuring the treatment of the epilepsy and reducing the side effect. In many cases, the effect of the traditional vision positioning method is not satisfactory. The signal processing method can provide a large amount of information to compensate for the deficiency of the visual diagnosis of the brain electrical signal. The location of the epilepsy area is considered as the problem of the identification of the driver, and a new non-linear cross-dependency measure _ weighted rank interdependency is proposed as the indicator of the driver because it can extract the coupling information from the brain electrical signal, in particular the coupling direction information. The PID controller is then used to carry out the seizure control. The epileptic seizure control method first needs to use the weighted rank interdependency to identify the epilepsy area. The two unidirectionally connected neuron group models are used to simulate the proposed control scheme. Depending on the application, the sensitivity of the weighted rank mutual dependence can be adjusted by two parameters, and their respective influences are discussed respectively. The results of the simulation show that the identification of the epilepsy can be adapted to different types of epilepsy, and the recognition rate of 98.84% can be obtained for different epilepsy areas. The simulation also shows that the PID controller can well control the synchronization between the neuron groups. The neuron group model can be used as an effective complement of clinical experiments to study the neurophysiology, and has the advantages of low cost, flexible parameter adjustment, and the like, and the research of the invention can help to promote the application of the neuron group model in the more field. In addition, on the basis of the neuron group model, the excitability, the synchronicity control and the focus recognition simulation can provide the theoretical basis for the design of the external attack control equipment, and can be further applied to the clinical application.
【學(xué)位授予單位】：山東大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：R742.1
，
本文編號(hào)：2508388
[Abstract]:Epilepsy is a common disease of the brain, which has the characteristics of renaturation, bursty and so on. The seizure is caused by the highly excited and highly synchronized discharges of the neurons, which are characterized by an irregular over-discharge of the high amplitude in the electroencephalogram. Therefore, the study of the pathogenesis and treatment of epilepsy has been a major problem and research focus of neurology. The neuron group model with the total coefficient can produce a waveform similar to that of the measured brain electrical signal, and the model parameters have a certain physiological significance, and a bridge is set up between the signal processing method and the research result of the neurophysiology. This also provides a platform for our study of the physiological mechanism of the seizure and its control. On the basis of the improvement of the neuron group model, the following studies are carried out on the physiological characteristics, the excitability and the synchronicity of the epileptic seizure and the control scheme of the synchronicity:1) In order to analyze the neurophysiological mechanism underlying the brain electrical signal, The brain electrical signal is considered as the output of a single neuron group and a plurality of neuron groups, and then the model parameters corresponding to the brain electrical signals in different periods of the epileptic seizure are identified, and the difference of the neurophysiological mechanism during the different period of the epileptic seizure is discussed by comparing the distribution of the model parameters. First, the brain electrical signal is considered as the output of a single neuron group (Wendling) model, and the delay unit and the gain unit are introduced on the basis of the Wendling model in order to simulate the influence of the measurement time and the device gain on the waveform of the brain electrical signal in the measurement of the brain electrical signal. The determination of the improved Wendling model parameter can be considered as an optimization problem, and the genetic algorithm is used to solve the optimal model parameter combination to minimize the error between the simulated brain electrical signal and the measured brain electrical signal. The model parameters corresponding to the different brain electrical signals during the onset of the seizure and the onset of the attack were determined by the method presented in this paper. The experimental results show that the modified Wendling model can well simulate the measured EEG signal, and the model parameter determination method based on the genetic algorithm has certain stability. The model parameters corresponding to the brain electrical signals during the onset and the attack period were compared, and the differences of the excitability and inhibition of the neurons during the different period of the seizure were discussed. The brain electrical signal is regarded as the output of a neuron group model, and the neuron in the neuron group has a uniform neurophysiological parameter, which can cause the model output signal component to be simple because the difference between the neurons is ignored. In order to better fit the measured brain electrical signal, a multi-neuron group model for parallel connection is proposed. The multi-neuron group model comprises a plurality of neuron groups, and the model output is a linear combination of the output of each neuron group. The measured brain electrical signal is considered as the output of the multi-neuron group model, and the number of the neurons in the model is not fixed, but the number of the neurons is minimized on the premise of ensuring that the waveform is sufficiently matched. The above problems can be simplified into a constrained lo-norm minimization problem, and the problem is solved by using the orthogonal matching tracking method. The experimental results showed that the number of the neurons in the attack period was significantly less than that of the attack, while the intensity of the main neuron group was significantly higher than that in the intermittent period. This indicates that in the course of the attack, there will be a process of fusion of the neurons, and a large number of similar neurons accumulate in a neuron group leading to high amplitude distribution. In addition, it can be seen from the experimental results that the intensity of the excitation and the intensity distribution of the EEG data during the onset and the onset of the episode are not very different, But the excitement/ suppression ratio of the onset period is somewhat increased.2) The excessive excitement of the nervous system has been seen as the main cause of the onset of the seizure, and when the self-regulation capacity of the nervous system is not sufficient to maintain the excitability-inhibitory balance, the seizure can be initiated. In order to control the seizure caused by over-excitation of the neurons, two strategies were developed. One is to reduce the excitability of the over-excited neurons, and the other is to increase the inhibition to compensate for the insufficiency of the nervous system's own regulation. The epileptic seizure degree was described by the sex index, and the controlled parameters of the PID controller were used to control the seizure. The changes of the seizure degree (seizure index) caused by the increase of excitability were simulated with the neuron group model, and the two kinds of excitability control strategies were simulated. The results show that the increase of the excitation intensity and the retention of the inhibitory intensity can lead to a significant increase in the epileptic index, resulting in a seizure. Using the PID controller to reduce the excitation intensity or the increase of the inhibition intensity, the excitation-suppression balance can be maintained, and the onset of the seizure is relieved.3) The seizure is always accompanied by a super-synchronous discharge of a plurality of neuron groups, which is represented as an irregular over-discharge of the high amplitude in the brain electrical signal. In order to study the hypersynchronous discharge mechanism of the seizure, and to control the seizure according to the synchronicity, a model including a plurality of neuron groups with a series relationship was constructed. The synchronicity of the two points is discussed and the coupling structure of the whole area cannot be reflected because the synchronism can be transmitted. By adopting the PCA algorithm, the concept of the synchronous family is introduced, and the calculation method of the synchronous family intensity and the participation rate is given. on the basis of the concept of the group of neuron groups, the invention provides an epileptic synchronization control scheme which does not carry out the identification of the epileptic focus, but also carries out a unified control on the synchronous family affected by the seizure, and breaks the coupling between each pair of neuron groups in the synchronous family, Thereby weakening the synchronicity between the neuronal populations. The results show that the PCA-based method can well identify the existing synchronous family and the neuron group in each of the synchronous families. The control strategy for the synchronous family can also control the seizure.4) The accurate location of the epilepsy area is the primary task of ensuring the treatment of the epilepsy and reducing the side effect. In many cases, the effect of the traditional vision positioning method is not satisfactory. The signal processing method can provide a large amount of information to compensate for the deficiency of the visual diagnosis of the brain electrical signal. The location of the epilepsy area is considered as the problem of the identification of the driver, and a new non-linear cross-dependency measure _ weighted rank interdependency is proposed as the indicator of the driver because it can extract the coupling information from the brain electrical signal, in particular the coupling direction information. The PID controller is then used to carry out the seizure control. The epileptic seizure control method first needs to use the weighted rank interdependency to identify the epilepsy area. The two unidirectionally connected neuron group models are used to simulate the proposed control scheme. Depending on the application, the sensitivity of the weighted rank mutual dependence can be adjusted by two parameters, and their respective influences are discussed respectively. The results of the simulation show that the identification of the epilepsy can be adapted to different types of epilepsy, and the recognition rate of 98.84% can be obtained for different epilepsy areas. The simulation also shows that the PID controller can well control the synchronization between the neuron groups. The neuron group model can be used as an effective complement of clinical experiments to study the neurophysiology, and has the advantages of low cost, flexible parameter adjustment, and the like, and the research of the invention can help to promote the application of the neuron group model in the more field. In addition, on the basis of the neuron group model, the excitability, the synchronicity control and the focus recognition simulation can provide the theoretical basis for the design of the external attack control equipment, and can be further applied to the clinical application.
【學(xué)位授予單位】：山東大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：R742.1
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本文編號(hào)：2508388
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