發(fā)布時間：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中,胎兒心電信號信噪比低且常受母體心電和其他強噪聲干擾,因此如何提取清晰的胎兒心電信號一直是胎兒監(jiān)護的重要研究課題。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混合模型且未充分利用心電信號特征信息,導致提取結(jié)果依然存在準確率低且生理學意義模糊等問題。為此,本論文結(jié)合信號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提取算法。具體內(nèi)容如下:(1)分析胎兒心電信號特性并構(gòu)建線性卷積混合模型。由電生理特性入手進行胎兒心電信號特性分析,利用非最小相位特性驗證了采用盲反卷積法提取胎兒心電信號的合理性;結(jié)合實際心電信號特性和盲反卷積源分離混合模型,對腹壁心電信號構(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提取方法。(2)基于循環(huán)平穩(wěn)特性和信號卷積混合模型,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延和循環(huán)頻率參數(shù)引入到基于非高斯性度量的目標函數(shù)構(gòu)造中,并通過梯度法最大化目標函數(shù)實現(xiàn)了母胎心電單源信號的有效分離。為提高胎兒心電信號估計準確度,利用最小二乘逆濾波母體心電消除算法改進傳統(tǒng)BSS中的時域相減法。實驗仿真表明,在母胎時域波形重疊情況下,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去噪預處理、特征參數(shù)估計和胎心電信號提取增強三部分的胎兒心電信號提取方法。分析實驗提取結(jié)果并將半盲反卷積源分離法與傳統(tǒng)BSS進行對比,實驗結(jié)果顯示,利用半盲反卷積源分離法提取的胎兒心電信號與實驗數(shù)據(jù)庫胎兒頭皮電極采集的胎兒心電參考信號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價指標方面優(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.
【學位授予單位】：重慶大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：R714.5;TN911.7

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