本文選題：心率變異性 + R波檢測(cè)��； 參考：《中南大學(xué)》2014年博士論文

【摘要】：摘要：連續(xù)竇性心拍之間的時(shí)間間隔存在微小漲落,這種現(xiàn)象稱為心率變異性(Heart Rate Variability, HRV)。 HRV蘊(yùn)藏了豐富的生理和病理信息,是評(píng)估自主神經(jīng)系統(tǒng)功能的一個(gè)重要標(biāo)志。HRV分析在疾病診斷、情緒識(shí)別和腦力負(fù)荷評(píng)估等諸多領(lǐng)域有著廣泛的應(yīng)用。本文對(duì)HRV信號(hào)的獲取、分析和應(yīng)用中的一些相關(guān)問(wèn)題進(jìn)行了研究。論文的主要研究?jī)?nèi)容如下： 1)提出了基于小波系數(shù)模極大值序列躍變點(diǎn)的R波檢測(cè)策略,實(shí)現(xiàn)了連續(xù)小波變換對(duì)心電信號(hào)R波的檢測(cè)。利用復(fù)Morlet小波與Mexican-hat小波對(duì)心電信號(hào)進(jìn)行連續(xù)小波變換后,小波系數(shù)模極大值對(duì)應(yīng)R波峰值的特點(diǎn),通過(guò)基于小波系數(shù)模極大值序列躍變點(diǎn)的R波檢測(cè)策略,在上述兩種小波系數(shù)的線性組合中檢測(cè)R波,平均靈敏度為99.37%,平均陽(yáng)性預(yù)測(cè)率為99.35%。 2)提出了基于CEEMD分解的RR間期序列去趨勢(shì)方法。從心電信號(hào)中提取的RR問(wèn)期序列是HRV分析的信息來(lái)源,并且是非均勻采樣的。為了得到準(zhǔn)確的HRV分析結(jié)果,需要在預(yù)處理階段將RR間期序列中緩慢的趨勢(shì)予以去除。平滑先驗(yàn)方法(Smoothness Prior Approach, SPA)目前使用最為廣泛,但這一方法需要將非均勻采樣的RR間期序列通過(guò)重采樣轉(zhuǎn)換為均勻采樣序列。這一過(guò)程將產(chǎn)生噪聲,并使信號(hào)的質(zhì)量受到損害。為了解決這一問(wèn)題,引入了經(jīng)驗(yàn)?zāi)B(tài)分解(Empirical Mode Decomposition, EMD)。將分解后的信號(hào)通過(guò)部分重構(gòu),去除其趨勢(shì)成分。這一方法可直接用于非均勻采樣信號(hào)的處理。此外,為了能夠采用標(biāo)準(zhǔn)指標(biāo)評(píng)價(jià)去趨勢(shì)方法的性能,提出了一個(gè)RR間期序列模型。采用以分貝計(jì)的信噪比(ISNR)、均方誤差(EMS)和百分比均方根誤差(DPRS)評(píng)價(jià)RR間期序列的去趨勢(shì)性能。結(jié)果表明,與SPA方法相比,基于互補(bǔ)整體EMD(Complementary Ensemble EMD, CEEMD)的去趨勢(shì)方法能得到更高的ISNR,更低的EMS和DPRS,說(shuō)明其具有更好的性能,并能由此得到更準(zhǔn)確的HRV分析結(jié)果。 3)比較了心衰病人和健康人的HRV指標(biāo),并建立了基于相關(guān)指標(biāo)的心衰診斷模型。采用時(shí)域、頻域和非線性方法對(duì)40名健康人和40名心衰病人的心電數(shù)據(jù)進(jìn)行了短時(shí)HRV分析,從而建立了基于不同指標(biāo)組合和線性判別分析(Linear Discriminant Analysis, LDA),及支持向量機(jī)(Support Vector Machine, SVM)的心衰診斷模型。結(jié)果表明,基于RR間期均值RR、RR間期標(biāo)準(zhǔn)差SDNN、去趨勢(shì)波動(dòng)分析(Detrended Fluctuation Analysis, DFA)短期波動(dòng)斜率α1、DFA長(zhǎng)期波動(dòng)斜率α2、近似熵ApEn等5個(gè)指標(biāo)和LDA的診斷模型診斷正確率可達(dá)到92.5%；基于RR、SDNN、RR間期差值的均方根RMSSD、Poincare分析短軸參數(shù)SD1、ApEn等5個(gè)指標(biāo)和SVM的模型診斷正確率可達(dá)到95%。HRV的相關(guān)指標(biāo)可揭示心臟的動(dòng)力學(xué)特征,并可用于心衰的診斷。 4)研究了足跟取血造成的疼痛暴露對(duì)新生兒自主神經(jīng)系統(tǒng)的影響,并建立了基于HRV指標(biāo)組合的新生兒疼痛檢測(cè)模型。采用時(shí)域、頻域和非線性方法對(duì)40名新生兒疼痛暴露前后心電數(shù)據(jù)進(jìn)行了短時(shí)HRV分析,并建立了基于不同指標(biāo)組合和LDA,及SVM的疼痛檢測(cè)模型。結(jié)果表明,基于ApEn、遞歸圖分析最大對(duì)角線長(zhǎng)度Lmax、確定性DET等3個(gè)指標(biāo)和LDA的新生兒疼痛檢測(cè)模型檢測(cè)正確率達(dá)到78.75%,基于RR、相鄰兩個(gè)RR間期對(duì)差值大于50ms的百分比pNN50、ApEn、關(guān)聯(lián)維D2、遞歸率REC等5個(gè)指標(biāo)和SVM的模型檢測(cè)正確率達(dá)到83.75%。HRV的相關(guān)指標(biāo)可反映新生兒自主神經(jīng)系統(tǒng)對(duì)疼痛暴露的應(yīng)答,相關(guān)指標(biāo)的組合可用于新生兒疼痛檢測(cè)。
[Abstract]:Abstract: there are small fluctuations in the time interval between continuous sinus racket. This phenomenon is called Heart Rate Variability (HRV). HRV contains abundant physiological and pathological information. It is an important marker for evaluating the function of autonomic nervous system, which is an important marker of.HRV analysis in the diagnosis of disease, emotion recognition and brain load assessment. The domain is widely applied. This paper studies the acquisition, analysis and application of HRV signals. The main contents of the paper are as follows:
1) a R wave detection strategy based on the jump point of the wavelet coefficient modulus maximum sequence is proposed to detect the R wave of the ECG signal by continuous wavelet transform. After the continuous wavelet transform between the complex Morlet and Mexican-hat wavelets, the maximum value of the wavelet coefficient modulus corresponds to the peak value of the R wave, and is based on the modulus maximum of the wavelet coefficients. The R wave detection strategy of the value sequence jump point detects the R wave in the linear combination of the above two wavelet coefficients, the average sensitivity is 99.37%, and the average positive predictive rate is 99.35%.
2) the RR interval sequence detrending method based on CEEMD decomposition is proposed. The RR query sequence extracted from the ECG signal is the source of the HRV analysis information and is nonuniform sampling. In order to obtain accurate HRV analysis results, the slow trend in the RR interval need to be removed at the preprocessing stage. The smooth prior method (Smoothness Pr) is needed. IOR Approach, SPA) is currently the most widely used, but this method needs to convert the non uniform sampled RR interval sequence into the uniform sampling sequence through resampling. This process will produce noise and damage the quality of the signal. In order to solve this problem, the empirical mode decomposition (Empirical Mode Decomposition, EMD) will be introduced. The signal is partially reconstructed to remove its trend component. This method can be used directly for the processing of nonuniform sampling signals. In addition, a RR interval sequence model is proposed in order to evaluate the performance of the detrend method with standard index. The mean square error (EMS) and the root mean square error of the signal to noise ratio (ISNR), the mean square error (EMS) and the percentage mean square error are adopted. (DPRS) the detrending performance of the RR interval is evaluated. The results show that the detrending method based on the complementary integral EMD (Complementary Ensemble EMD, CEEMD) can get higher ISNR, lower EMS and DPRS, indicating that it has better performance and can get more accurate HRV analysis results from this method compared with the SPA method.
3) the HRV index of patients with heart failure and healthy people was compared, and a diagnostic model of heart failure based on related indexes was established. The time domain, frequency domain and nonlinear methods were used to analyze the ECG data of 40 healthy people and 40 heart failure patients by short time HRV analysis, which was based on the combination of different indexes and linear discriminant analysis (Linear Discriminant Anal). Ysis, LDA) and the diagnosis model of heart failure of Support Vector Machine (SVM). The results show that, based on the RR interval mean RR, RR interval standard deviation SDNN, detrending fluctuation analysis (Detrended Fluctuation), short-term wave slope alpha 1, long wave slope alpha 2, approximate entropy, and other 5 indexes and diagnostic model diagnosis The accuracy can reach 92.5%; the root mean square (RMS) RMSSD based on RR, SDNN and RR interval values, the Poincare analysis of the short axis parameter SD1, ApEn and so on, and the correct rate of the model diagnosis of SVM can reach 95%.HRV, which can reveal the dynamic characteristics of the heart, and can be used for the diagnosis of heart failure.
4) the effects of pain exposure on the heel extraction on the autonomic nervous system of the newborn were studied, and a neonatal pain detection model based on HRV index combination was established. The time domain, frequency domain and nonlinear methods were used to analyze the ECG data of 40 neonates before and after pain exposure, and a combination of different indexes and LDA was established. And SVM's pain detection model. The results show that, based on ApEn, recursive graph analysis of the maximum diagonal length Lmax, deterministic DET and other 3 indicators, and LDA for neonatal pain detection model detection accuracy reached 78.75%, based on RR, two adjacent RR intervals are more than 50ms in the percentage pNN50, ApEn, associated dimension D2, recursion REC, and other 5 indicators. The correlation index of the correct rate of 83.75%.HRV can reflect the response of the neonatal autonomic nervous system to pain exposure, and the combination of the related indicators can be used for neonatal pain detection.

【學(xué)位授予單位】：中南大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：R722.1;R541.6

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