本文關(guān)鍵詞： 帕金森病 震顫 信號(hào)檢測(cè) MEMS 功率譜分析　出處：《重慶理工大學(xué)》2017年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：帕金森病(Parkinson’s disease,PD)是一種常見(jiàn)的神經(jīng)系統(tǒng)變性疾病,起病隱襲,進(jìn)展緩慢。老年人多見(jiàn),平均發(fā)病年齡為60歲左右,該病會(huì)使患者逐漸喪失生活自理能力,對(duì)家庭對(duì)社會(huì)造成極大負(fù)擔(dān)。約70%的帕金森病患者以震顫為首發(fā)癥狀,而震顫是神經(jīng)科最常見(jiàn)的癥狀之一,是許多疾病或綜合征的首發(fā)表現(xiàn),疾病早期臨床上很難鑒別。臨床上醫(yī)生主要憑主觀判斷診斷分類(lèi)震顫,缺乏客觀評(píng)價(jià)標(biāo)準(zhǔn),容易造成誤診。震顫的準(zhǔn)確判斷和分類(lèi)可為診斷治療震顫類(lèi)疾病提供有價(jià)值信息,所以有必要對(duì)震顫進(jìn)行分類(lèi)和一定的量化評(píng)估。震顫的分類(lèi)量化問(wèn)題主要有震顫?rùn)z測(cè)和震顫分析兩方面工作。震顫?rùn)z測(cè)為獲取震顫信號(hào),震顫分析則是分析震顫信號(hào)特征,為識(shí)別分類(lèi)震顫提供相關(guān)參數(shù)。在震顫?rùn)z測(cè)方面加速度慣性傳感器檢測(cè)方法以其方便、快捷、無(wú)創(chuàng)、實(shí)時(shí)監(jiān)護(hù)等優(yōu)點(diǎn)成為近幾年震顫?rùn)z測(cè)的研究熱點(diǎn)。震顫分析方面頻域往往能提供更多的信號(hào)特征。因此本課題在參考前人研究的基礎(chǔ)上,選擇基于慣性加速度傳感器方法進(jìn)行震顫?rùn)z測(cè),通過(guò)分析震顫加速度信號(hào)功率譜特征,尋找相關(guān)特征參數(shù)來(lái)區(qū)別帕金森病震顫。本課題以輔助醫(yī)生客觀識(shí)別評(píng)價(jià)帕金森病震顫為目的,圍繞帕金森病震顫?rùn)z測(cè)方法及其震顫信號(hào)分析方法進(jìn)行相關(guān)研究,主要完成了工作概括為以下兩個(gè)方面:一是設(shè)計(jì)制作完成了震顫信號(hào)檢測(cè)系統(tǒng),該系統(tǒng)包括震顫信號(hào)采集端和震顫信號(hào)檢測(cè)操作系統(tǒng)。震顫信號(hào)采集端基于微機(jī)電系統(tǒng)(MEMS)技術(shù)的慣性傳感器制作而成,具備小巧便攜的特點(diǎn)。震顫信號(hào)檢測(cè)操作系統(tǒng)基于VB.NET開(kāi)發(fā)環(huán)境開(kāi)發(fā)完成,操作界面簡(jiǎn)潔。整個(gè)檢測(cè)系統(tǒng)以無(wú)創(chuàng)檢測(cè)方法實(shí)現(xiàn)了對(duì)震顫的實(shí)時(shí)監(jiān)測(cè)。二是通過(guò)制作的震顫信號(hào)檢測(cè)系統(tǒng)完成了30例震顫數(shù)據(jù)采集(20例病理性震顫和10例生理性震顫),對(duì)所得震顫數(shù)據(jù)進(jìn)行功率譜分析得出結(jié)論:1、震顫加速度信號(hào)幅度值可作為震顫程度劃分參數(shù),利用該參數(shù)對(duì)震顫的量化有可能為醫(yī)生在治療過(guò)程中判斷患者的用藥劑量提供幫助;2、發(fā)現(xiàn)帕金森病震顫、帕金森綜合征震顫和特發(fā)性震顫的功率譜能量集中頻率范圍有重疊,難以區(qū)分。而帕金森病靜止性震顫功率譜峰值頻率具有倍數(shù)關(guān)系特征,該特征可識(shí)別帕金森病靜止性震顫,有希望用于輔助診斷早期PD。
[Abstract]:Parkinsonian disease (PDD) is a common neurodegenerative disease, which begins quietly and progresses slowly. It is common in the elderly, with an average onset age of about 60 years, and the disease will gradually deprive patients of their ability to take care of themselves. About 70% people with Parkinson's disease start with tremor, one of the most common symptoms in neurology, and it is the first symptom of many diseases or syndromes. It is very difficult to distinguish the disease in the early clinical. In clinic, doctors mainly use subjective judgment to diagnose classified tremor, lack of objective evaluation standard, easy to cause misdiagnosis. The accurate judgment and classification of tremor can provide valuable information for diagnosis and treatment of tremor like disease. So it is necessary to classify and evaluate the tremor. The problem of classifying and quantifying the tremor mainly includes two aspects: tremor detection and tremor analysis. Tremor detection is to obtain the tremor signal, and the tremor analysis is to analyze the characteristics of the tremor signal. To identify the classification of tremor to provide relevant parameters. In tremor detection, acceleration inertial sensor detection method is convenient, fast, non-invasive, The advantages of real-time monitoring have become a hot topic in recent years. The frequency domain of tremor analysis can often provide more signal characteristics. The method based on inertial acceleration sensor is selected to detect tremor. The characteristics of power spectrum of vibration acceleration signal are analyzed. In order to identify and evaluate Parkinson's disease tremor objectively with the objective of assisting doctors to identify and evaluate the tremor, this study focused on the methods of detecting and analyzing the tremor signal of Parkinson's disease. The main work is summarized as follows: first, the tremor signal detection system is designed and manufactured. The system includes a tremor signal acquisition terminal and a tremor signal detection operating system. The tremor signal acquisition terminal is made from an inertial sensor based on the micro electromechanical system (MEMS) technology. Small portable features. Tremor signal detection operating system based on VB.NET development environment development, The operation interface is simple. The whole detection system realizes the real-time monitoring of tremor by non-invasive method. Second, 30 cases of pathological tremor and 10 cases of physiologic tremor are collected by the system. Conclusion: 1: 1, the amplitude of the tremor acceleration signal can be used as the parameter of the degree of tremor. The quantification of tremor by this parameter may be helpful for doctors to judge the dosage of drug used in patients during the course of treatment. It is found that the frequency range of power spectrum energy concentration in Parkinson's disease tremor, Parkinson's syndrome tremor and idiopathic tremor is overlapped. It is difficult to distinguish the peak frequency of power spectrum of static tremor in Parkinson's disease, which can be used to identify static tremor in Parkinson's disease and may be used to assist in early diagnosis of PD.
【學(xué)位授予單位】：重慶理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TN911.6;R742.5

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 鄧澤懷;劉波波;李彥良;;常見(jiàn)的功率譜估計(jì)方法及其Matlab仿真[J];電子科技;2014年02期

2 ;MEMS供電連接器令便攜式醫(yī)療設(shè)備更精巧[J];中國(guó)電子商情(基礎(chǔ)電子);2013年12期

3 張文波;王宇卉;;震顫的診斷和鑒別診斷策略[J];世界臨床藥物;2012年10期

4 張冬至;胡國(guó)清;;微機(jī)電系統(tǒng)關(guān)鍵技術(shù)及其研究進(jìn)展[J];壓電與聲光;2010年03期

5 艾玲梅;王玨;;基于EMD奇異值分解診斷震顫的新方法[J];生物醫(yī)學(xué)工程學(xué)雜志;2009年06期

6 艾玲梅;王玨;;基于雙譜分析和支持向量機(jī)的手震顫加速度信號(hào)識(shí)別[J];電子學(xué)報(bào);2008年11期

7 艾玲梅;黃力宇;王玨;;震顫信號(hào)分析的研究現(xiàn)狀及展望[J];生物醫(yī)學(xué)工程學(xué)雜志;2007年06期

8 孫麗君;顧平;王銘維;;不同類(lèi)型震顫的臨床特點(diǎn)、發(fā)生機(jī)制及影響因素(英文)[J];中國(guó)臨床康復(fù);2006年46期

9 張博;陳永冰;;基于PComm的串口通信實(shí)現(xiàn)[J];微計(jì)算機(jī)信息;2006年21期

10 金鈴;MEMS技術(shù)研究及應(yīng)用[J];現(xiàn)代雷達(dá);2004年12期

相關(guān)會(huì)議論文 前2條

1 陳生弟;;中國(guó)帕金森病治療指南(第三版)[A];江西省第七次中西醫(yī)結(jié)合神經(jīng)科學(xué)術(shù)交流會(huì)論文集[C];2015年

2 黃慧;劉昊;;震顫定性定量分析方法的研究進(jìn)展[A];江西省第六次中西醫(yī)結(jié)合神經(jīng)科學(xué)術(shù)交流會(huì)論文集[C];2013年

相關(guān)博士學(xué)位論文 前1條

1 孫建;基于外骨骼機(jī)器人技術(shù)的人體手臂震顫抑制關(guān)鍵技術(shù)研究[D];中國(guó)科學(xué)技術(shù)大學(xué);2010年

相關(guān)碩士學(xué)位論文 前1條

1 代海洋;人手生理性震顫測(cè)試關(guān)鍵技術(shù)研究[D];哈爾濱工程大學(xué);2013年

，

本文編號(hào)：1537563