【摘要】：心臟是保證人體生命活動(dòng)正常進(jìn)行的核心器官,它受到神經(jīng)、血壓、激素等多種因素的調(diào)節(jié)。近年來(lái),心臟疾病一直是威脅人類健康的主要疾病之一,對(duì)心臟功能進(jìn)行準(zhǔn)確的評(píng)估和診斷,成為一個(gè)重要的研究課題。 心電信號(hào)蘊(yùn)含著心臟電活動(dòng)豐富的生理信息,在臨床醫(yī)學(xué)與研究中易于采集和檢測(cè),具有較直觀的規(guī)律性,是當(dāng)前臨床醫(yī)學(xué)與生物科學(xué)領(lǐng)域研究最多的心臟電信號(hào)。從心電信號(hào)中提取的心率變異性(Heart rate variability, HRV)信號(hào)在近二十年的研究中受到了普遍的重視。作為反映自主神經(jīng)系統(tǒng)活動(dòng)水平的靈敏指標(biāo),對(duì)HRV的提取和分析已經(jīng)在定量評(píng)估交感神經(jīng)和副交感神經(jīng)活動(dòng)的緊張性、均衡性及其對(duì)心血管系統(tǒng)的影響方面得到了廣泛的應(yīng)用。 近年來(lái),熵分析法廣泛應(yīng)用到HRV的分析中,并取得了一定的進(jìn)展。該方法以其方法簡(jiǎn)單、運(yùn)算快速、抗干擾強(qiáng)等優(yōu)點(diǎn)為探測(cè)和捕捉時(shí)間序列中的有用信息提供了方便。由于心率變異性信號(hào)是非平穩(wěn)、有噪聲干擾的時(shí)間序列,所以本文采用非線性動(dòng)力學(xué)分析方法中的基本尺度熵方法,在前人研究的基礎(chǔ)上對(duì)HRV信號(hào)進(jìn)行了分析,主要研究工作和創(chuàng)新點(diǎn)如下： (1)研究了改變基本尺度熵方法中的延遲時(shí)間對(duì)熵值的影響。通過(guò)Logistic映射序列、1/f噪聲序列和HRV信號(hào)序列,發(fā)現(xiàn)延遲時(shí)間取L=1時(shí)基本尺度熵并沒(méi)達(dá)到最大值,當(dāng)L≥3時(shí),熵值收斂于一個(gè)固定值,該值非常接近理論推導(dǎo)值log2(4m)。因此,通過(guò)實(shí)驗(yàn)仿真得出最佳延遲時(shí)間參數(shù)為L(zhǎng)=3,此時(shí)基本尺度熵值能夠真實(shí)全面地反映時(shí)間序列的復(fù)雜程度,為HRV分析方法中其他參數(shù)的選取提供一定的參考價(jià)值。 (2)計(jì)算了五種不同生理、病理狀態(tài)人群的基本尺度熵值與“禁止?fàn)顟B(tài)”個(gè)數(shù),得出兩者的變化關(guān)系,并應(yīng)用多尺度化的基本尺度熵分析量化HRV序列在多個(gè)時(shí)間尺度下波動(dòng)的不規(guī)則度,得出其復(fù)雜性指數(shù)。結(jié)果表明健康年輕人的計(jì)算結(jié)果代表了最佳的生理健康狀態(tài),而其他生理、病理人群由于心臟病變,自主神經(jīng)系統(tǒng)紊亂,造成心率變異性的復(fù)雜性有所下降。 (3)設(shè)計(jì)、實(shí)施了顛倒作息的實(shí)驗(yàn),采集了六名測(cè)試者正常作息24h和顛倒作息24h的心電信號(hào),經(jīng)過(guò)數(shù)據(jù)預(yù)處理,從中提取HRV信號(hào)。聯(lián)合基本尺度熵、MSE曲線和m-words組合形式分布直方圖分析顛倒作息情況下睡眠、清醒兩種狀態(tài)的HRV信號(hào),與正常作息下的變化規(guī)律進(jìn)行比較。結(jié)果表明人體睡眠清醒循環(huán)比晝夜節(jié)律24小時(shí)交替循環(huán)對(duì)心臟的搏動(dòng)特性影響更大,清醒和睡眠狀態(tài)決定了自主神經(jīng)系統(tǒng)的相互作用規(guī)律與HRV信號(hào)混沌特性,同時(shí)對(duì)人體本身動(dòng)力學(xué)復(fù)雜性產(chǎn)生影響。
[Abstract]:The heart is the core organ which guarantees the normal human life activity. It is regulated by many factors such as nerve, blood pressure, hormone and so on. In recent years, heart disease has been one of the main diseases threatening human health. The accurate evaluation and diagnosis of heart function has become an important research topic. Electrocardiogram (ECG), which contains abundant physiological information of cardiac electrical activity, is easy to collect and detect in clinical medicine and research, and has more intuitive regularity. It is the most researched ECG signal in the field of clinical medicine and biological science. Heart rate variability (Heart rate variability, HRV) signals extracted from ECG signals have received widespread attention in recent 20 years. As a sensitive index to reflect the level of autonomic nervous system activity, the extraction and analysis of HRV have been widely used in the quantitative evaluation of sympathetic and parasympathetic nervous system tension, balance and its influence on cardiovascular system. In recent years, entropy analysis has been widely used in the analysis of HRV, and some progress has been made. This method provides convenience for detecting and capturing useful information in time series because of its simple method, fast operation and strong anti-interference. Because the signal of heart rate variability is non-stationary and noisy time series, in this paper, the basic scale entropy method of nonlinear dynamics analysis is used to analyze the HRV signal on the basis of previous studies. The main research works and innovations are as follows: (1) the effect of delay time on entropy is studied. By using 1 / f noise sequence and HRV signal sequence of Logistic mapping sequence, it is found that the entropy of the basic scale of delay time L = 1 does not reach the maximum. When L 鈮，

本文編號(hào)：2195788