用小波变换的模极大值提取胎儿心率的方法

对胎儿监护的主要方法是监听胎儿心率,而超声多普勒测量胎儿心率是一种很好的无创方法.但是,由于测量得到的原始信号成分非常复杂,干扰严重,从而使其对胎心率的提取造成很大困难.本文利用小波变换系数的模的平方值与信号奇异性指数之间的关系,从超声回波信号中提取出了胎儿的心率.由于噪声的小波变换系数随尺度的增大而减小,因此,该方法具有较高的抗干扰能力.通过模拟仿真和实际信号处理,证明该方法能准确地从超声多普勒信号中提取胎心率信号.     

一种多谱勒胎音信号提取方法的研究

对胎儿监护的主要方法是监听胎儿心率，而超声多谱勒测量胎儿心率是一种很好的无创方法。但是，由于测量得到的原始信号成分非常复杂，干扰严重，从而使其对胎心率的提取造成很大困难。文章提出一种新的胎音信号处理方法——平均频移自相关方法：先在移动的时间间隔内对超声多谱勒信号进行傅立叶变换，得到信号的时频分布，然后求对应的每个频谱的平均频率得到多谱勒信号的平均频率曲线，最后利用自相关算法从平均频率曲线中提取胎儿心脏跳动曲线。通过模拟仿真和实际信号处理，证明该方法能准确地从超声多谱勒信号中提取胎心率信号。     

小波变换去噪方法在多谱勒胎儿心率提取中的应用研究

为了从超声多谱勒胎音信号中提取胎音信号，获得平滑的胎心率曲线，并计算胎心率，必须去除信号提取过程中的各种干扰和噪声。对平均频移算法获得的多谱勒信号的平均频率曲线采用小波变换中的五层强制去噪方法，使后续的相关计算能得到平滑的胎音信号，从而方便计算胎儿的心率。通过对实际超声多谱勒胎音信号的处理，获得了较好的结果，提取出了稳定、平滑的胎心率曲线，较准确地计算出了胎心率。     

基于MyDAQ和LabVIEW的胎心音实验系统

目的：指出胎儿监护的重要性和通过检测胎心音实现胎儿监护的重要价值，设计一个胎心音实验平台，实现胎心音采集和性质分析。方法：首先依据WHO的调查，世界每年有数百万胎儿死亡，若孕妇在怀孕期间按周期做胎儿健康监护，则可以明显降低婴儿的死亡率，简述了胎儿健康监护的重要意义；然后比较多种胎儿监护方法，指出胎心音图法具有更重要的价值。最后论文分析了胎心音的产生和基本特点，基于MyDAQ和LabVIEW建立了一个胎心音实验系统。结果：该实验系统实现了胎心音的采集、WAV格式存储和读取。时变滤波器和小波除噪的预处理，以及基于TSA的胎心音性质分析，得出胎心音的性质与文献研究一致。结论：该系统廉价、快速原型化、灵活的优势，以及能够开放的特点，不仅能用于胎心音的相关研究，还适用于语音等信号的采集和分析。     

模板优选结合非线性模板匹配多普勒胎心率提取方法

超声多普勒胎心心率测量方法是目前胎心心率计数的金标准,但现有各种胎心仪的胎心率提取算法在第二产程母体干扰较强的情况下容易发生误检。针对这一不足,本文提出了优选模板结合非线性模板匹配的方法,该方法通过聚类建立时频帧模板库,然后从模板库中选取模板对信号进行模板匹配。在对信号进行短时傅里叶变换后,逐帧对信号与模板的差进行带泄露线性整流(LReLU)函数的最优化,以优化结果作为模板匹配度。最后,对匹配度曲线进行峰值提取,获取胎心率。本研究将提出的方法与自相关法进行对比,结果显示本文方法比自相关方法的检出正确率平均提高20%,23%的样本正确率提高50%以上。本文通过分析干扰与信号发生混合时的特点设计算法,希望通过本文为胎心率提取提示一种不仅仅关注信号层面的新思路。     

基于移动智能终端的单通道胎儿心电监护系统

目的 设计基于移动智能终端的单通道胎儿心电监护系统,以实现扩展卡尔曼滤波(extended Kalman filtering,EKF)和奇异值分解(singular value decomposition,SVD)相结合的单通道胎儿心电提取算法,实时获取高信噪比的胎儿心电信号,完成胎心监护的远程移动医疗.方法 利用STM32单片机控制24位采样芯片ADS1298,对单通道的孕妇腹部信号进行采集,并将采集后的数据经蓝牙传送给移动智能终端,在基于Android的移动智能终端上实现EKF和SVD相结合的单通道胎儿心电提取算法,完成对胎儿心电的实时提取、显示、存储与分析,计算心律变异率,实现对整个监护系统进行控制等功能.结果 测试结果表明,该系统可从单通道孕妇腹部信号中准确提取出胎儿心电信号,准确度为95.60％,阳性预测率为98.71％,系统工作稳定,连续处理5个胎心周期的数据用时约为70μs,小于一个母体心动周期(约0.8 s)的时间,适于临床对胎儿心电的实时监护.结论 该系统实时性强、准确率高、工作稳定、操作简单、便于携带,实现了对胎心监护的可穿戴式远程移动医疗,适合社区医院和家庭使用.     

分娩监护分析系统的研制

分娩监护分析系统由宫缩压力传感器，宫缩信号检测处理电路，多普勒超声探头，胎心率检测处理电路，微型计算机系统等构成，其软件包括实时监测，预诊分析，实验分析和系统维护四大模块，系统可以实时检测，存储和图文显示分娩过程中的胎心率和宫外宫缩信息，所存储的现场信息可以动态回放，作回顾分析研究。     

胎心率信号分析的进展

胎心率监护是围产期胎儿监护的一项重要内容，过去对胎心率基线的评估仅限于通过手工测量与计算和肉眼识别的方法来判定胎心率监护指标，随着计算机技术，人工智能及网络技术在我国的发展，基于计算机的各种新的计算方法，测量手段也逐步用于胎心率的分析和研究中，使得胎心率能够更准确地反映胎儿状况，提高胎心率监护的监床价值，胎心率的远程监护适用了现代医学信息管理的发展趋势。     

数码远程监护在高危妊娠中的临床应用

数码胎儿远程监护,是通过电话将孕妇的胎心音及胎动信息传入医院,由医护人员进行监护,随时发现异常可随时处理。目前,为新兴的胎心率监护手段之一,在高危妊娠的管理监测中,数码胎儿远程监护是医护人员监护院外孕妇的首选手段之一。结合我院临床应用情况,现分析如下:资料与方法1     

生物医学信号的广义时域分析

广义时频表示方法在处理非平稳信号中发挥了越来越重要的作用，并在众多领域中有广泛的应用前景。本文在介绍时频分析的基本方法之后，综述了该方法在生物医学信号（如：超声多普勒血流信号、心音、心血管音、肌音、脑电及诱发电位、心电、晚电位等）处理中的应用。     

Passive fetal monitoring by adaptive wavelet denoising method

Fetal Heart Rate (FHR) monitoring is one of the most important fetal well being tests. Existing FHR monitoring methods are based on Doppler ultrasound technique, which has several disadvantages. Passive fetal monitoring by phonocardiography is an appropriate alternative; however, its implementation is a challenging task due to low energy of fetal heart sounds and multiple interference signals presence. In this paper, an advanced signal processing method for passive fetal monitoring based on adaptive wavelet denoising is presented. The method's performance is compared with Doppler ultrasound monitor. The results show 94-97.5% accuracy, including highly disturbed cases.     

The information content of Doppler ultrasound signals from the fetal heart

Knowledge of the content of Doppler ultrasound signals from the fetal heart is essential if the performance of fetal heart rate (FHR) monitors based upon this technology is to be improved. For this reason instrumentation was constructed to enable the simultaneous collection of Doppler audio signals and the transabdominal fetal ECG (for signal registration), with a total of 22 recordings being made with an average length of around 20 minutes. These data demonstrate the transient nature of the Doppler audio data with wide variations in the signal content observable on a beat-to-beat basis. Short-time Fourier analysis enabled the content of the Doppler signals to be linked to six cardiac events, four valve and two wall motions, with higher frequency components being associated with the latter. This differing frequency content together with information regarding the direction of movement that can be discerned from Doppler signals provided a potential means of discriminating between these six events (which are unlikely to all contribute to the Doppler signal within the same cardiac cycle). Analysis of 100 records showed that wall contractions generate the most prominent signals, with atrial contraction recognisable in all records and ventricular wall contraction in 95% (although its amplitude is only around 30% of that of the atrial signal). Valve motion, with amplitudes between 15 and 25% that of the atrial wall signal, were visible in 75% of records. These results suggest means by which the six events that contribute to the Doppler signal may be distinguished, providing information that should enable an improvement in the current performance of Doppler ultrasound-based FHR monitors.     

一种基于自相关和平均模差算法的胎儿心率提取方法

目的 胎儿心率是判断孕期胎儿健康状况的一项重要指标,使用超声多普勒测量胎心率是常用的无创方法.其中,自相关算法是常用的测量胎心率的算法,但是其抗噪性差,容易出错.方法 本文提出一种基于自相关与平均标准模差相结合的新算法,先通过自相关函数突显其周期性,然后用累计平均标准模差函数提取准确周期,最后通过使用该算法处理模拟器数据和临床数据,验证其精确度和抗噪性.结果 相比滑动窗算法,基于自相关和平均标准模差的算法具有更高的精确度和抗噪性.结论 改进后的算法可以有效计算出胎心率值.     

一种基于单通道腹部信号的胎儿心电提取算法

设计一种基于单通道孕腹部信号的胎儿心电提取算法,分别提取出母亲心电和胎儿心电,并计算出母亲心率和胎儿心率。首先对单通道孕腹部信号进行k-TEO(k=19)变换,突出母亲心电的QRS波,从而通过简单的阈值法确定母亲心电的R波位置,接着通过在相邻R波间重采样以获得相同的R-R间期T,这样经过一个间隔为T的梳状滤波器就可以分离出相同R-R间期的母亲心电,然后再一次在相邻R波间进行重采样恢复原来的R-R间期就可以获得实际的母亲心电了。原始腹部信号减去上面提取的母亲心电后,胎儿心电QRS波的信噪比大大提高,通过再次应用提取母亲心电的算法即可得到“干净”的胎儿心电波形。选取Physionet数据库中的8 组(26 通道)孕腹部信号数据进行分析,计算每个通道数据的胎儿心电QRS波位置识别灵敏度、阳性检测率和准确性。结果表明,胎儿心电QRS波的识别准确率达到87.1%,其中有6 个通道达到100%。另外计算每个通道的母亲心率和胎儿心率并做统计分析,发现每一组中各个通道的母亲平均心率和胎儿平均心率都非常接近,同一组中各通道间母亲平均心率最大误差为0.1次/min, 而胎儿平均心率最大误差也只有0.9次/min,进一步证明算法的可靠性。     

The influence of coincidence of fetal and maternal QRS complexes on fetal heart rate reliability

Bioelectrical fetal heart activity being recorded from maternal abdominal surface contains more information than mechanical heart activity measurement based on the Doppler ultrasound signals. However, it requires extraction of fetal electrocardiogram from abdominal signals where the maternal electrocardiogram is dominant. The simplest technique for maternal component suppression is a blanking procedure, which relies upon the replacement of maternal QRS complexes by isoline values. Although, in case of coincidence of fetal and maternal QRS complexes, it causes a loss of information on fetal heart activity. Its influence on determination of fetal heart rate and the variability analysis depends on the sensitivity of the heart-beat detector used. The sensitivity is defined as an ability to detect the incomplete fetal QRS complex. The aim of this work was to evaluate the influence of the maternal electrocardiogram suppression method used on the reliability of FHR signal being calculated.     

Fetal heart rate monitoring based on independent component analysis

In this paper, an algorithm based on independent component analysis (ICA) for extracting the fetal heart rate (FHR) from maternal abdominal electrodes is presented. Three abdominal ECG channels are used to extract the FHR in three steps: first preprocessing procedures such as DC cancellation and low-pass filtering are applied to remove noise. Then the algorithm for multiple unknown source extraction (AMUSE) algorithm is fed to extract the sources from the observation signals include fetal ECG (FECG). Finally, FHR is extracted from FECG. The method is shown to be capable of completely revealing FECG R-peaks from observation leads even with a SNR=-200dB using semi-synthetic data.     

一种便携式多普勒胎心检测系统的设计及实现

本研究设计并实现了一款便携式,全数字,用于围产期胎儿心率检测的多普勒胎心系统,其采用对超声回波加窗,数字相乘等方法获取频移信号,并可对特定组织深度进行运动检测。该系统由CPLD超声激励及采集电路、MCU及外围电路和系统软件三部分组成。与两款商用胎心仪相比,本系统准确度较高,能够稳定可靠的对胎心进行检测。     

Assessment of autonomic function in the late term fetus: The effects of sex and state

Researchers have reported associations between fetal sex and heart rate (FHR) and heart rate variability (FHRV) but rarely in the context of fetal behavioral sleep state. We examined differences in measures of fetal autonomic function by sex and sleep state. Fetal abdominal ECG monitoring technology was used to measure FHR and two measures of FHRV—standard deviation of FHR (SD) and beat-to-beat variability (RMSSD). FHR and movement patterns were also recorded with standard Doppler ultrasound monitor technology employed to code sleep states. Data were collected from 82 healthy fetuses ranging from 36 to 39 weeks gestation. A one-way MANOVA showed that FHR was significantly lower and SD was significantly higher for males than females. Independent samples t tests found that these sex differences were only in the active sleep state. There were no significant differences in RMSSD by sex. Repeated measures MANOVA for a subset that exhibited more than one state (N = 22) showed that SD was significantly different by state. RMSSD showed a marginally significant sleep state difference. In conclusion, fetal sex differences in HR and HRV may indicate more mature autonomic functioning in near-term males than females and fetal sleep state can influence abdominal fECG derived measures of FHR and FHRV.     

Nonlinear analysis of the performance and reliability of wavelet singularity detection based denoising for Doppler ultrasound fetal heart rate signals

Many studies on the physiology of the cardiovascular system revealed that nonlinear chaotic dynamics govern the generation of the heart rate signal. This is also valid for the fetal heart rate (FHR) variability, where however the variability is affected by many more factors and is significantly more complicated than for the adult case. Recently an adaptive wavelet denoising method for the Doppler ultrasound FHR recordings has been introduced. In this paper the performance and reliability of that method is confirmed by the observation that for the wavelet denoised FHR signal, a deterministic nonlinear structure, which was concealed by the noise, becomes apparent. It provides strong evidence that the denoising process removes actual noise components and can therefore be utilized for the improvement of the signal quality. Hence by observing after denoising a significant improvement of the 'chaoticity' of the FHR signal we obtain strong evidence for the reliability and efficiency of the wavelet based denoising method. The estimation of the chaoticity of the FHR signal before and after the denoising is approached with three nonlinear analysis methods. First, the rescaled scale analysis (RSA) technique reveals that the denoising process increases the Hurst exponent parameter as happens when additive noise is removed from a chaotic signal. Second, the nonlinear prediction error evaluated with radial basis function (RBF) prediction networks is significantly lower at the denoised signal. The significant gain in predictability can be attributed to the drastic reduction of the additive noise from the signal by the denoising algorithm. Moreover, the evaluation of the correlation coefficient between actual and neural network predicted values as a function of the prediction time displays characteristics of chaos only for the denoised signal. Third, a chaotic attractor, reconstructed with the embedding dimension technique, becomes evident for the denoised signal, while it is completely obscured for the original signals. The correlation dimension of the reconstructed attractor for the denoised signal tends to reach a value independent of the embedding dimension, a sign of deterministic chaotic signal. In contrast for the original signal the correlation dimension increases steadily with the embedding dimension, a fact that indicates strong contribution of noise.