本文選題：癲癇 + 閉環(huán)控制　； 參考：《天津大學(xué)》2014年碩士論文

【摘要】：癲癇是大腦神經(jīng)元突發(fā)性異常放電導(dǎo)致短暫的大腦功能障礙的一種慢性神經(jīng)系統(tǒng)疾病,針對(duì)藥物無(wú)法控制的難治性癲癇,電刺激正逐漸成為其有效的治療手段之一,如深度腦刺激(DBS)、經(jīng)顱直流電刺激(tDCS)等。目前臨床使用的開(kāi)環(huán)電刺激方法并不具有普遍適用性,為解決這個(gè)問(wèn)題,通過(guò)閉環(huán)電刺激來(lái)治療癲癇等精神疾病已成為目前研究的熱點(diǎn)。由于癲癇疾病的致病機(jī)理尚不清楚加之電生理實(shí)驗(yàn)方面的各種限制,神經(jīng)計(jì)算模型分析已成為研究神經(jīng)系統(tǒng)動(dòng)力學(xué)特性和生理現(xiàn)象的有效方法之一。因此,基于單神經(jīng)元和神經(jīng)元集群模型,本文提出了閉環(huán)迭代學(xué)習(xí)控制(ILC)策略與閉環(huán)混合控制策略,以實(shí)現(xiàn)癲癇態(tài)放電的閉環(huán)控制。本論文的研究?jī)?nèi)容主要包括以下三個(gè)方面:首先,本文基于海馬CA3區(qū)Pinsky-Rinzel(PR)模型和大腦皮層神經(jīng)元集群模型(NMM),分析其動(dòng)力學(xué)特性,考慮單神經(jīng)元關(guān)鍵參數(shù)—鉀離子通道反電勢(shì)V_K、胞體與樹(shù)突之間的耦合電導(dǎo)cg、化學(xué)耦合強(qiáng)度g_(NMDA)、g_(AMPA)對(duì)神經(jīng)元放電模式的影響;神經(jīng)元集群參數(shù)—興奮性平均突觸增益A、耦合強(qiáng)度K_(ij)對(duì)神經(jīng)元集群放電的影響,探索癲癇發(fā)病機(jī)制。其次,基于關(guān)鍵參數(shù)對(duì)單神經(jīng)元放電模式的影響,利用迭代學(xué)習(xí)控制、延時(shí)反饋與PI控制相結(jié)合的混合控制分別實(shí)現(xiàn)了單神經(jīng)元癲癇樣放電的閉環(huán)控制;采用無(wú)跡卡爾曼濾波器(UKF)對(duì)關(guān)鍵參數(shù)進(jìn)行估計(jì),進(jìn)而形成反饋信號(hào)。通過(guò)對(duì)關(guān)鍵參數(shù)的跟蹤反饋調(diào)節(jié)實(shí)現(xiàn)PR神經(jīng)元放電模式的轉(zhuǎn)換;基于化學(xué)耦合強(qiáng)度的影響,利用迭代學(xué)習(xí)控制實(shí)現(xiàn)不同耦合強(qiáng)度的神經(jīng)元間放電去同步控制。最后,基于多耦合神經(jīng)元集總參數(shù)模型提出多模式閉環(huán)深度腦刺激系統(tǒng)。依據(jù)興奮性平均突觸增益與耦合強(qiáng)度的影響,產(chǎn)生癲癇態(tài)與正常態(tài)下的局部場(chǎng)電位(LFP)波形;根據(jù)對(duì)C_0復(fù)雜度、信號(hào)頻帶能量比的分析,提取局部場(chǎng)電位的特征;采用BP神經(jīng)網(wǎng)絡(luò)對(duì)局部場(chǎng)電位信號(hào)進(jìn)行分類(lèi)并實(shí)現(xiàn)刺激強(qiáng)度的選擇;針對(duì)不同的放電模式利用迭代學(xué)習(xí)控制器輸出控制信號(hào);通過(guò)對(duì)刺激電流幅值和周期的調(diào)制輸出精準(zhǔn)的深度腦刺激電流,實(shí)現(xiàn)癲癇狀態(tài)的閉環(huán)控制,阻止癲癇樣放電的進(jìn)一步傳播。以上的研究結(jié)果均證明本文提出的閉環(huán)控制策略在控制癲癇發(fā)作狀態(tài)上的有效性,為單神經(jīng)元及神經(jīng)元集群閉環(huán)電生理實(shí)驗(yàn),以及癲癇電刺激治療器的研究提供了理論依據(jù)。
[Abstract]:Epilepsy is a kind of chronic nervous system disease, which is caused by sudden abnormal discharge of brain neurons. Electrical stimulation is becoming one of the effective treatment methods for intractable epilepsy, which can not be controlled by drugs.For example, deep brain stimulation, transcranial direct current stimulation (TDC), and so on.In order to solve this problem, closed loop electrical stimulation has become a hot research topic in the treatment of mental disorders such as epilepsy.As the pathogenesis of epilepsy is not clear and various limitations in electrophysiological experiments, neural computing model analysis has become one of the effective methods to study the dynamic characteristics and physiological phenomena of nervous system.Therefore, based on the single neuron and neuron cluster model, a closed loop iterative learning control (ILC) strategy and a closed loop hybrid control strategy are proposed to realize the closed loop control of epileptic state discharge.The main contents of this thesis include the following three aspects: firstly, based on the Pinsky-Rinzelpra model of hippocampal CA3 region and the cerebral cortex neuron cluster model, the dynamic characteristics of the model were analyzed.Consider the effect of the key parameter of single neuron VK, the coupling conductance between the cell body and the dendrite, the chemical coupling intensity, the chemical coupling intensity, on the firing pattern of the neurons.The effect of neuronal cluster parameters-excitatory average synaptic gain A, coupling intensity K _ (+ +) on the discharge of neuronal cluster and the pathogenesis of epilepsy were explored.Secondly, based on the influence of key parameters on the single neuron discharge mode, the closed-loop control of single neuron epileptoid discharge is realized by the hybrid control of iterative learning control, delay feedback and Pi control.Unscented Kalman filter (UKF) is used to estimate the key parameters to form feedback signals.Based on the influence of chemical coupling intensity, iterative learning control is used to realize the de-synchronous control of firing between neurons with different coupling intensity.Finally, a closed loop deep brain stimulation system is proposed based on the multi-coupled neuron lumped parameter model.According to the effect of excitatory average synaptic gain and coupling intensity, the local field potential (LFP) waveform was generated in epileptic state and normal state, and the characteristic of local field potential was extracted according to the complexity of CSP 0 and the analysis of signal frequency band energy ratio.BP neural network is used to classify the local field potential signals and to select the stimulus intensity, and the iterative learning controller is used to output the control signals for different discharge modes.By modulating the amplitude of the stimulus current and the period of the stimulus, the amplitude of the stimulation current and the modulation of the cycle output the accurate deep brain stimulation current to realize the closed-loop control of the epileptic state and to prevent the further spread of the epileptoid discharge.The above results all prove the effectiveness of the closed-loop control strategy proposed in this paper in the control of epileptic seizures. It provides a theoretical basis for the research of single neuron and neuron cluster closed loop electrophysiological experiment and the therapeutic device of epileptic electrical stimulation.
【學(xué)位授予單位】：天津大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：R742.1

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