發(fā)布時(shí)間：2019-03-21 13:22

【摘要】：胎兒心電圖是目前最為有效的圍產(chǎn)期胎兒監(jiān)護(hù)手段之一。醫(yī)生通過分析胎兒心電波形變化及早發(fā)現(xiàn)胎兒在宮內(nèi)發(fā)育時(shí)產(chǎn)生的病變情況,從而降低初生嬰兒損傷率和死亡率。但非侵入式法采集的孕婦腹壁混合心電信號(hào)中,胎兒心電信號(hào)信噪比低且常受母體心電和其他強(qiáng)噪聲干擾,因此如何提取清晰的胎兒心電信號(hào)一直是胎兒監(jiān)護(hù)的重要研究課題。盲源分離(BSS)是目前最具研究前景的胎兒心電信號(hào)提取算法,然而現(xiàn)有基于BSS的胎心電提取算法通常采用線性瞬時(shí)混合模型且未充分利用心電信號(hào)特征信息,導(dǎo)致提取結(jié)果依然存在準(zhǔn)確率低且生理學(xué)意義模糊等問題。為此,本論文結(jié)合信號(hào)特性和混合模型提出了一種基于卷積模型的半盲胎兒心電信號(hào)提取算法。具體內(nèi)容如下:(1)分析胎兒心電信號(hào)特性并構(gòu)建線性卷積混合模型。由電生理特性入手進(jìn)行胎兒心電信號(hào)特性分析,利用非最小相位特性驗(yàn)證了采用盲反卷積法提取胎兒心電信號(hào)的合理性;結(jié)合實(shí)際心電信號(hào)特性和盲反卷積源分離混合模型,對(duì)腹壁心電信號(hào)構(gòu)建線性卷積混合模型并給出基于心電特性的胎兒心電信號(hào)提取方法。(2)基于循環(huán)平穩(wěn)特性和信號(hào)卷積混合模型,提出了半盲反卷積源分離算法。半盲反卷積單源分離算法將時(shí)延和循環(huán)頻率參數(shù)引入到基于非高斯性度量的目標(biāo)函數(shù)構(gòu)造中,并通過梯度法最大化目標(biāo)函數(shù)實(shí)現(xiàn)了母胎心電單源信號(hào)的有效分離。為提高胎兒心電信號(hào)估計(jì)準(zhǔn)確度,利用最小二乘逆濾波母體心電消除算法改進(jìn)傳統(tǒng)BSS中的時(shí)域相減法。實(shí)驗(yàn)仿真表明,在母胎時(shí)域波形重疊情況下,最小二乘逆濾波母體心電消除算法較之傳統(tǒng)時(shí)域相減法在胎兒心電波形保留完整性方面效果更好。(3)基于半盲反卷積源分離算法原理,提出了完整的胎兒心電信號(hào)提取方法。結(jié)合半盲反卷積單源分離和母體心電消除算法,提出了包括混合心電信號(hào)去噪預(yù)處理、特征參數(shù)估計(jì)和胎心電信號(hào)提取增強(qiáng)三部分的胎兒心電信號(hào)提取方法。分析實(shí)驗(yàn)提取結(jié)果并將半盲反卷積源分離法與傳統(tǒng)BSS進(jìn)行對(duì)比,實(shí)驗(yàn)結(jié)果顯示,利用半盲反卷積源分離法提取的胎兒心電信號(hào)與實(shí)驗(yàn)數(shù)據(jù)庫胎兒頭皮電極采集的胎兒心電參考信號(hào)在胎心率和波形均拍方面極為接近,并且在可視化波形和信噪比等性能評(píng)價(jià)指標(biāo)方面優(yōu)于傳統(tǒng)BSS。
[Abstract]:Fetal electrocardiogram is one of the most effective perinatal fetal monitoring methods. By analyzing the changes of fetal ECG waveform, we can find out the pathological changes of fetus during intrauterine development as early as possible, so as to reduce the rate of injury and mortality of newborn infants. However, the signal-to-noise ratio of fetal ECG signal is low and often interfered by maternal ECG and other strong noises in the mixed ECG signals collected by non-invasive method in pregnant women's abdominal wall. Therefore, how to extract clear fetal ECG signals has always been an important research topic in fetal monitoring. Blind source separation (BSS) is the most promising fetal ECG signal extraction algorithm at present. However, the existing BSS-based FECG extraction algorithms usually use the linear instantaneous mixed model and underutilize the ECG signal feature information. As a result, there are still some problems, such as low accuracy and vague physiological meaning. In this paper, a semi-blind fetal ECG signal extraction algorithm based on convolution model is proposed based on signal characteristics and mixed model. The main contents are as follows: (1) analyze the characteristics of fetal ECG signal and construct the linear convolution mixed model. By analyzing the electrophysiological characteristics of fetal ECG signals, the rationality of using blind deconvolution method to extract fetal ECG signals was verified by using non-minimum phase characteristics. Combining the characteristics of actual ECG signals and the mixed model of blind deconvolution source separation, The linear convolution hybrid model of abdominal ECG signal is constructed and the method of fetal ECG signal extraction based on ECG characteristics is presented. (2) based on the mixed model of cyclostationary and signal convolution, a semi-blind deconvolution source separation algorithm is proposed. Semi-blind deconvolution single-source separation algorithm introduces delay and cyclic frequency parameters into the construction of objective function based on non-Gaussian metric, and achieves the effective separation of single-source signal of maternal fetal ECG by maximizing the objective function of gradient method. In order to improve the accuracy of fetal ECG signal estimation, the least square inverse filtering algorithm is used to improve the time domain subtraction method in traditional BSS. The simulation results show that when the waveform of the mother-fetus overlaps in time domain, The least square inverse filtering algorithm is better than the traditional time domain subtraction method in preserving the integrity of fetal ECG waveform. (3) based on the principle of semi-blind deconvolution source separation algorithm, A complete method of fetal ECG signal extraction was proposed. Combined with semi-blind deconvolution single source separation and maternal ECG cancellation algorithm, a new method of fetal ECG signal extraction is proposed, which includes mixed ECG signal de-noising pre-processing, feature parameter estimation and fetal ECG signal extraction and enhancement. The experimental results were analyzed and the semi-blind deconvolution source separation method was compared with the traditional BSS. The experimental results showed that The fetal ECG signal extracted by the semi-blind deconvolution source separation method is very close to the fetal ECG reference signal collected by the fetal scalp electrode in both fetal heart rate and waveform. And it is better than traditional BSS. in visual waveform and signal to noise ratio.
【學(xué)位授予單位】：重慶大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：R714.5;TN911.7

【相似文獻(xiàn)】

相關(guān)期刊論文 前3條

1 張軼文;;反卷積法用于單光子發(fā)射計(jì)算機(jī)層析X射線攝影(SPECT)中散射補(bǔ)償與窗減法技術(shù)的比較[J];國(guó)外醫(yī)學(xué).生物醫(yī)學(xué)工程分冊(cè);1988年04期

2 翟洪昌;郭雪萍;翁旭初;崔淑范;祝一虹;;健康成人手指運(yùn)動(dòng)腦功能成像數(shù)據(jù)聚類與反卷積的比較研究[J];中國(guó)臨床康復(fù);2005年44期

3 ;[J];;年期

相關(guān)會(huì)議論文 前6條

1 吳新星;李明;李目海;單佩韋;;網(wǎng)絡(luò)微積分中最小加反卷積的研究[A];2008'中國(guó)信息技術(shù)與應(yīng)用學(xué)術(shù)論壇論文集（一）[C];2008年

2 盧少平;;基于盲反卷積的圖像上采樣算法[A];第六屆和諧人機(jī)環(huán)境聯(lián)合學(xué)術(shù)會(huì)議（HHME2010)、第19屆全國(guó)多媒體學(xué)術(shù)會(huì)議（NCMT2010）、第6屆全國(guó)人機(jī)交互學(xué)術(shù)會(huì)議（CHCI2010）、第5屆全國(guó)普適計(jì)算學(xué)術(shù)會(huì)議（PCC2010）論文集[C];2010年

3 李棟棟;郭學(xué)彬;瞿安連;徐濤;;應(yīng)用三維熒光反卷積顯微技術(shù)觀察活體細(xì)胞[A];中國(guó)生理學(xué)會(huì)第21屆全國(guó)代表大會(huì)暨學(xué)術(shù)會(huì)議論文摘要匯編[C];2002年

4 熊俊;李棟棟;瞿安連;;活體細(xì)胞四維圖像科學(xué)可視化方法的研究[A];中國(guó)生物醫(yī)學(xué)工程學(xué)會(huì)第六次會(huì)員代表大會(huì)暨學(xué)術(shù)會(huì)議論文摘要匯編[C];2004年

5 蘇兆鋒;楊海亮;孫劍鋒;叢培天;王亮平;;“強(qiáng)光一號(hào)”加速器能譜測(cè)量實(shí)驗(yàn)中的波形還原問題初探[A];第十二屆反應(yīng)堆數(shù)值計(jì)算與粒子輸運(yùn)學(xué)術(shù)會(huì)議論文集[C];2008年

6 李育杉;戴憲華;劉軍;;一種時(shí)變MIMO系統(tǒng)盲反卷積模型[A];第十屆全國(guó)信號(hào)處理學(xué)術(shù)年會(huì)（CCSP-2001）論文集[C];2001年

相關(guān)博士學(xué)位論文 前2條

1 孫小君;最優(yōu)和自校正多傳感器信息融合白噪聲反卷積估值器[D];黑龍江大學(xué);2010年

2 婁帥;多尺度變換域圖像反卷積理論研究[D];哈爾濱工業(yè)大學(xué);2010年

相關(guān)碩士學(xué)位論文 前10條

1 李鶴;利用反卷積提高布里淵光纖傳感系統(tǒng)空間分辨率的研究[D];哈爾濱工業(yè)大學(xué);2015年

2 孫世豹;基于傳遞函數(shù)評(píng)價(jià)的共焦顯微圖像反卷積降噪處理算法研究[D];哈爾濱工業(yè)大學(xué);2015年

3 馬威鋒;CLAD反卷積方法中噪聲抑制效應(yīng)的探究[D];南方醫(yī)科大學(xué);2015年

4 鄒岸;聽覺誘發(fā)電位的一種刺激序列優(yōu)化技術(shù)及反卷積方法[D];南方醫(yī)科大學(xué);2015年

5 袁雪寒;基于反卷積網(wǎng)絡(luò)的圖像模式學(xué)習(xí)及應(yīng)用[D];華南理工大學(xué);2016年

6 陶姣;基于半盲反卷積源分離的胎兒心電信號(hào)提取研究[D];重慶大學(xué);2016年

7 蔡曉燕;醫(yī)學(xué)超聲信號(hào)反卷積研究[D];東南大學(xué);2006年

8 陳揚(yáng)鈦;反卷積網(wǎng)絡(luò)圖像表述與復(fù)原[D];國(guó)防科學(xué)技術(shù)大學(xué);2011年

9 郭淑貞;基于高階譜和小波分析的超聲醫(yī)學(xué)圖像反卷積研究[D];東南大學(xué);2005年

10 王佳偉;統(tǒng)一和通用的信息融合白噪聲反卷積估值器[D];黑龍江大學(xué);2007年

，

本文編號(hào)：2444972