發(fā)布時(shí)間：2018-06-07 00:53

  本文選題：腦血流生理調(diào)節(jié) + 最小角度準(zhǔn)則　； 參考：《哈爾濱工業(yè)大學(xué)》2015年碩士論文

【摘要】：在血壓變化正常、可控范圍內(nèi),大腦內(nèi)的血流將保持相對(duì)恒定,這是由腦血流自動(dòng)調(diào)節(jié)機(jī)能實(shí)現(xiàn)的。調(diào)節(jié)能力一旦減弱,將引起重大危險(xiǎn)性疾病。更重要的是,往往在疾病出現(xiàn)癥狀之前,調(diào)節(jié)能力特征參數(shù)早已發(fā)生改變。臨床若能夠準(zhǔn)確的測(cè)量自動(dòng)調(diào)節(jié)生理系統(tǒng),便可以通過(guò)優(yōu)化手段來(lái)調(diào)控血壓,積極有效的改善患者的治療方式。傳統(tǒng)測(cè)量方法是建立一個(gè)單輸入單輸出或二輸入多輸出模型,利用自適應(yīng)最小二乘辨識(shí)算法,通過(guò)系統(tǒng)權(quán)系數(shù)序列評(píng)估調(diào)節(jié)機(jī)能的特征參數(shù):系統(tǒng)相位差或調(diào)節(jié)機(jī)能指數(shù)。但存在于調(diào)控與評(píng)估過(guò)程中的變異性會(huì)對(duì)特征參數(shù)的辨識(shí)產(chǎn)生消極影響,而這是由權(quán)系數(shù)序列中無(wú)關(guān)項(xiàng)干擾引起的。模型簡(jiǎn)單、抗干擾能力差,信號(hào)成分復(fù)雜、特征不明顯都會(huì)產(chǎn)生無(wú)關(guān)項(xiàng),致使評(píng)估結(jié)果出現(xiàn)穩(wěn)態(tài)誤差和動(dòng)態(tài)波動(dòng)。同時(shí),以往方法中相位評(píng)估結(jié)果單一、不具有時(shí)域特征,調(diào)節(jié)機(jī)能指數(shù)評(píng)估為整數(shù)類(lèi)型、精確度較低。因此,傳統(tǒng)方法在評(píng)估調(diào)節(jié)機(jī)能上效果不理想,得到的辨識(shí)曲線誤差較大,波動(dòng)明顯。為了減小變異性,一種高效的評(píng)估方法顯得尤為重要。本課題通過(guò)研究、探索,建立了不同于以往的二輸入單輸出模型,彌補(bǔ)了抗干擾能力不足的缺點(diǎn);實(shí)現(xiàn)高分辨率的高斯濾波,對(duì)數(shù)據(jù)進(jìn)行關(guān)鍵處理步驟,減弱噪聲干擾;應(yīng)用最小角度準(zhǔn)則,減少無(wú)關(guān)項(xiàng)存在,實(shí)現(xiàn)最小角度一次算法與自適應(yīng)算法,高效控制穩(wěn)態(tài)誤差和動(dòng)態(tài)波動(dòng),變異性整體上減小。同時(shí),實(shí)現(xiàn)相位差希爾伯特算法和調(diào)節(jié)機(jī)能指數(shù)非整數(shù)算法,將辨識(shí)結(jié)果最優(yōu)化。為了采集到最佳特征狀態(tài)的生理信號(hào),搭建了一個(gè)下肢束帶平臺(tái),完全的誘發(fā)調(diào)節(jié)機(jī)能,降低錯(cuò)誤辨識(shí)的概率。最后進(jìn)行仿真數(shù)據(jù)和實(shí)驗(yàn)數(shù)據(jù)分析,并對(duì)比傳統(tǒng)方法,驗(yàn)證了新評(píng)估方法的有效性。
[Abstract]:The blood flow in the brain remains relatively constant in a controlled and normal range of blood pressure, which is achieved by the automatic regulation of cerebral blood flow. Once the regulation ability weakens, will cause the serious dangerous disease. More importantly, regulatory characteristics often change long before symptoms appear. If the automatic physiological system can be measured accurately, the blood pressure can be adjusted by optimizing means, and the treatment mode of patients can be improved actively and effectively. The traditional measurement method is to establish a single input, single output or two input multiple output model. The adaptive least square identification algorithm is used to evaluate the characteristic parameters of the regulating function by the weight coefficient sequence of the system: the system phase difference or the regulating function index. However, the variability in the process of regulation and evaluation can have a negative impact on the identification of characteristic parameters, which is caused by the interference of irrelevant terms in the weight coefficient series. The model is simple, the anti-interference ability is poor, the signal component is complex, and the characteristics are not obvious, which will cause the stable error and dynamic fluctuation of the evaluation results. At the same time, in the previous methods, the phase evaluation results are single and have no time domain characteristics. The adjustment function index evaluation is of integer type, and the accuracy is low. Therefore, the traditional method is not effective in evaluating the regulation function, the error of the identification curve is large and the fluctuation is obvious. In order to reduce variability, an efficient evaluation method is particularly important. Through the research and exploration, the paper establishes a two-input single-output model which is different from the past, which makes up for the deficiency of anti-jamming ability, realizes the high-resolution Gao Si filter, carries on the key processing step to the data, reduces the noise interference; The minimum angle criterion is applied to reduce the existence of independent terms and to realize the minimum angle primary algorithm and adaptive algorithm. The steady-state error and dynamic fluctuation are effectively controlled and the variability is reduced as a whole. At the same time, the phase difference Hilbert algorithm and the non-integer algorithm of adjusting the function index are implemented to optimize the identification results. In order to collect the physiological signal of the best characteristic state, a lower limb banding platform was built to induce the regulation function completely and reduce the probability of error identification. Finally, the simulation data and experimental data are analyzed, and compared with the traditional methods, the effectiveness of the new evaluation method is verified.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類(lèi)號(hào)】：TN911.7;R741

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本文編號(hào)：1988939