基于非负盲分离的胎儿心率检测方法

胎儿心率监测是一种有效评估胎儿当前健康状况的重要参考依据。为了可以快速准确地获取胎儿心率,该文提出一种基于非负盲分离的胎儿心率检测方法。该方法首先对采集得到的腹壁信号进行预处理,平稳小波变换后重构出母亲心电信号;接着,采用相减法去除母亲心电信号,再把剩下含有噪声的胎儿心电信号通过时频变换得到Born-Jordan分布;最后,利用非负矩阵分解得到胎儿心电的特征信号,检测其R波位置求得胎儿瞬时心率。实验结果表明,该方法可以快速、准确有效地获得胎儿地瞬时心率数据。     

基于结合小波变换与FastICA算法的脑电信号降噪(英文)

该文提出一种结合小波变换(WPT)与快速独立分量分析(Fast ICA)算法的方法来分析脑电信号。首先,原始脑电信号是通过使用WPT分解为三个层。然后,设置第三层最高频率的系数为零,以减少脑电信号的随机噪声,同时尽可能的保留信号的细节。其次,采取快速独立分量分析算法的优势,从脑电信号中分离所有类型的噪声。提出一种准预期值(QEV)的方法确定脑电图信号来自何处。最后,为了检验系统的性能,所有信道的相关信号在快速独立分量分析的输出进行分析。实验结果证实,交叉相关系数是10-15或10-16的量级,几乎可以被视为零。所提出性能良好的方法可以去除脑电信号所有类型的噪声。     

应用自相关和独立分量分析的胎儿心电信号提取

目的:胎儿心电信号在监护胎儿健康状况过程中有着重要的作用。通常从孕妇腹部采集到的混合心电信号中提取出胎儿心电信号,孕妇腹部信号是准周期性的时间信号,其采样点存在着先后关系,传统的独立分量分析(ICA)算法在分离过程中没有考虑信号的时间相关性,针对这一问题提出了一种新的方法提取胎儿心电信号。方法:首先采用自相关分析可以得到混合信号具体的周期长度,根据周期长度进行片段截取信号后可以去除其时间相关性,再利用传统的FastICA分离截取信号得到ICA模型的模型参数,最后利用此模型参数从完整的混合信号中提取出胎儿心电信号。结果:使用临床数据进行了实验验证,分别使用传统的FastICA和新的方法提取胎儿心电信号,结果表明采用新方法提取出的胎儿心电信号中母体成分干扰得到了很好的抑制,胎儿心电信号比较清晰,分离效果优于传统的FastICA。结论:该方法可以清晰地提取出胎儿心电信号,在胎儿心电信号提取中具有很高的实用价值。     

基于扩展Infomax 独立分量分析算法的脑电信号消噪

背景：脑电信号能够反映大脑不同的生理病理状态,但在采集和分析处理过程中极易受到各种噪声的干扰,如眼球运动、眨眼、心电、肌电等,这些噪声的存在严重影响了脑电信号的分析和处理。 目的：介绍了一种基于扩展Infomax的独立分量分析方法,并用于脑电信号消噪。 方法：通过扩展Infomax算法的迭代求得分离矩阵,采用去除噪声分量后的独立成分重构需要记录的脑电信号,观察Matlab仿真得到的去噪后的脑电信号,同时比较去噪前后各导联脑电信号与眼电信号的相关性。 结果与结论：使用扩展Infomax 独立分量分析算法能够成功地去除多导脑电信号中的眼电干扰。再比较去噪前后各导联脑电信号的功率谱,可以发现使用扩展Infomax独立分量分析算法同时也能够成功地去除多导脑电信号中的工频干扰,且对脑电信号中的其他有用信号几乎没有破坏。     

基于小波变换和改进的瞬态独立成分分析融合算法的心电信号降噪方法

本文提出一种基于小波变换与独立成分分析融合的信号处理方法,该方法用于抑制多通道同步采集的心电信号包含的噪声。首先利用小波变换对各路同步采集的原始心电信号进行八尺度分解,获得低频逼近信号与高频细节信号,通过设定阈值的方法去除属于低频噪声部分的逼近信号。然后对保留的细节信号进行反变换实现信号重构,再利用包含预同步功能的瞬态独立成分分析改进算法从重构的信号中分离出高频噪声与心电信号独立成分。最后采用信噪比与均方根误差作为信号质量评价指标,将融合算法与单独使用瞬态独立成分分析算法的处理结果进行对比,结果表明融合算法进行降噪处理这一方法具有更高的信噪比和更低的均方根误差,本文提出的融合算法具有良好的心电信号降噪性能。     

独立分量分析在表面肌电信号分解中的应用

采用独立分量分析中的信息极大化快速算法初步探讨了表面肌电信号的分解问题。研究结果表明 ,独立分量分析对肌肉轻度收缩力水平下 (<10 %MVC)表面肌电信号的分解有较好的效果 ,可以作为表面肌电信号分解的一种预处理手段     

基于卷积混合盲源分离技术的表面肌电信号分解研究

根据表面肌电信号（SEMG）形成的生理学特性，采用一种基于卷积混合过程的盲源分离技术来分析隐含在SEMG信号中的运动单位动作电位信息，利用仿真的SEMG信号对这种算法的分解性能进行实验研究，并与采用瞬时混合过程的独立分量分析（ICA）算法的分解性能进行比较，同时将该算法应用于真实SEMG信号的分解实验。研究结果表明，无论是对模拟SEMG信号还是真实SEMG信号，采用卷积混合盲源分离技术的分解方法均能得到较明显的分解效果，且该方法较符合表面肌电信号的形成过程，因而具有重要的研究价值。     

基于抗混叠小波变换的胎儿心电信号分离方法

目的胎儿心电图能够较好地反映胎儿在子宫内的发育状况,但是由于采集的胎儿心电信号中混有噪声干扰,给医学诊断带来极大干扰。抗混叠小波变换算法能够从混有噪声干扰的源信号中提取胎儿心电信号,且当胎儿心电信号与母体心电信号混叠时,该方法仍能够提取胎儿心电信号。基于此,本文提出一种基于抗混叠小波变换的胎儿心电信号分离方法。方法首先对原始心电信号进行滤波预处理,再利用小波变换分离母体心电信号和胎儿心电信号,最后根据抗混叠分离算法获取混合心电信号中的胎儿心电信号,得到满周期的胎儿心电信号。结果该方法能够较好地获取胎儿心电波形,胎儿心电波形识别准确率可达100%,在信噪比较低的情况下,识别准确率仍可达到77.78%。应用此算法在国外MIT-BIT心电信号数据和国内医院临床心电信号数据中进行实验仿真,并与先前学者的胎儿心电信号提取方法进行对比。结论此方法具有较高的识别准确率以及在临床应用中的可靠性和可行性。     

基于独立分量分析的生理信号盲源分离

用于盲源分离的独立分量分析 (ICA)和扩展ICA算法 ,基于极大似然估计 ,给出一个衡量输出分量统计独立的目标函数 ,最优化该目标函数 ,得到一种用于独立分量分析的迭代算法。扩展ICA算法的优点在于迭代过程中不需要计算信号的高阶统计量 ,收敛速度快 ,同时适用于超高斯和亚高斯信号的分离。应用该算法实现了脑电、心电信号以及语音信号的分离 ,并给出了实验结果     

基于带参考信号的ICA算法的脑电信号眨眼伪差的分离研究

独立分量分析（ICA）是一种从混合信号中提取统计独立的分量的一种方法.本研究提出了一种基于带参考信号的ICA算法的脑电信号眨眼伪差的分离方法,可以得到纯净的脑电信号.这个方法的主要思路是：先选取一导眨眼伪差比较明显的数据,从中获得眨眼伪差的参考信号,再用ICA方法把眨眼伪差第一个提取出来,最后得到消除伪差后的EEG信号.详细讨论了使用带参考信号的ICA算法消除眨眼伪差的方法与步骤,并给出了应用于真实信号的实验结果.     

基于独立成分分析和量子粒子群算法的胎儿心电提取

胎儿心电图(FECG)是反映胎儿心脏电生理活动的一项客观指标,获取的FECG受到母体心电图(MECG)的干扰,如何快捷、有效的提取FECG成为重要的研究课题。在非侵入方式下,FECG的提取算法中独立成分分析(ICA)算法被认为是效果最好的方法,但现有求解其分解矩阵的算法收敛性能都不太高。量子粒子群(QPSO)算法是一种收敛于全局的智能优化算法。因此,提出了一种结合QPSO的ICA方法。研究结果表明,与其他在非侵入方式下的主要提取算法相比,这种方法能更清晰准确地提取出有用信号,为胎儿的健康检测提供了更好的方法。     

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.     

The fetal electrocardiogram by independent component analysis and wavelets

Once the fetal electrocardiogram (FECG) waveforms from ECG on the maternal abdomen are detected, the fetal P wave and T wave cannot always be identified by using continuous wavelet transform (CWT). We took noninvasive FECG from the maternal abdomen, extracted it from the maternal electrocardiogram waveforms after an Independent Component Analysis (ICA), and identified the features of those waveforms by using CWT. We also simultaneously analyzed the observed signals by Primary Component Analysis (PCA). FECG has been extracted by ICA from 25 of 30 pregnant women. The fetal P wave and T wave could be identified in 21 of the 25 cases. FECG was extracted by PCA in only one case. ICA is superior to PCA, whose separation quality highly depends on the careful positioning of the electrodes. We believe that after ICA, FECG obtained by the wavelet theory based method will become a powerful tool for the differential diagnosis of fetal arrhythmias.     

两导心电信号独立分量分析算法的研究

根据独立分量分析(ICA)理论,要想在观测信号中提取出独立分量,观测信号的数目必须大于或等于独立分量的数目,因此要求采用ICA算法的胎儿心电图机导联数必须大于一定数目,但在实际应用中常常难以满足这个条件。故本文提出了一种基于少数导联心电(ECG)信号的胎儿心电(FECG)提取算法,结合FECG和自适应噪声抵消算法,从两导采集于孕妇腹部体表的ECG信号中提取FECG。实验表明,该方法能够获得清晰的FECG信号。     

400例围产胎儿心电图检测分析及异常心电图的处理

本文应用ＦＥＣＧ－Ｄ型仪对４００例围产胎儿进行了ＦＥＣＧ的检测及分析。结果成功３６３例，成功率为９０．８％，正常ＦＥＣＧ３１６例占成功数的８７％，异常ＦＥＣＧ４７例占成功数的１３％。在对异常ＦＥＣＧ的类型及原因的分析中发现，异常ＦＥＣＧ中以ＦＳＴ下移＞５ｕｖ为多占５３．２％，其次为胎儿心动过速２５．６％，胎儿心动过缓１０．６％。胎儿心律不齐６．３％，ＦＱＲＳ时限增宽４．３％。胎儿心电图异常的原因为胎儿窘迫占７８．７％，脐带绕颈１２．８％，提示胎儿宫内缺氧是引起围产ＦＥＣＧ异常的主要原因，２２例（４７％）异常ＦＥＣＧ分别经胎盘给药、吸氧、输液等处理转为正常。     

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。     

基于经验模态分解自适应滤波的胎儿心电信号提取

目的提出了一种基于经验模态分解自适应滤波的胎儿心电信号提取法。方法首先利用经验模态分解算法对孕妇腹部信号进行分解得到一组内模函数(IMF),然后将这组IMF作为自适应滤波器的主输入信号,并将孕妇胸部信号作为参考输入信号。通过学习算法自适应组合IMF,滤除母体心电信号成分,从而提取胎儿心电信号。结果与结论基于仿真和临床的实验结果表明,该方法提取的胎儿心电信号误差小,性能优于传统的最小均方和归一化最小均方自适应滤波算法。     

Comparison of SVD methods to extract the foetal electrocardiogram from cutaneous electrode signals

The paper presents and compares three methods making use of the singular value decomposition (SVD) of a matrix to extract the foetal electrocardiogram (FECG) from cutaneously recorded electrode signals. The first method constructs a set of orthogonal foetal signals (the so-called principal foetal signals) from the recordings, but needs electrode positions far from the foetal heart, in addition to the abdominal electrodes that pick up a mixture of maternal and foetal electrocardiogram. An online adaptive algorithm has been developed such that a real-time implementation becomes feasible. The second method is a new online approach to a technique presented by van Oosterom. Although this method has some important drawbacks and is suboptimal as far as foetal signal-to-noise ratio is concerned. it is still very useful when only a foetal trigger is required, as the signal obtained is not a complete FECG, Finally, a third method is proposed, based on the generalised SVD and interpreted with the new concept of oriented signal-to-signal ratio. An online version is also presented for this method and some results are shown.     

Detection of fetal electrocardiogram signals using matched filters with adaptive normalisation

The authors discuss the application of matched filters to the detection of R-waves in fetal electrocardiogram (FECG) data, recorded during labour using a scalp electrode. When using the basic matched filter, one correlates a template representing the clean signal with the noisy signal. This method is optimal when the underlying noise is white in nature. However, it is known that false detection of R-waves can occur in the presence of extraneous peaks which have a similar shape to the fetal R-wave. It is proposed to switch between two different normalisations of the impulse response of the matched filter to alleviate this problem. When the signal-to-noise ratio is lower than a predetermined threshold, then normalisation to the geometric mean of the template and noisy data energies is carried out, otherwise only normalisation to the template energy is made. In the former case, the background noise and spikes that are larger than the underlying FECG are attenuated, hence increasing the probability of detection of the R-waves. In the latter case, noise which has a lower amplitude than the underlying R-wave, is reduced. The effectiveness of this method is demonstrated by application to scalp electrode data.     

Ultra-low-noise instrumentation and computer-based data acquisition and processing system for obtaining a noninvasive and intrauterine catheter fetal electrocardiogram

A fetal electrocardiogram (FECG) from the abdominal surface will be from 20 microVpp to unmeasurable. The intrauterine catheter signal will be from 50 microVpp to unmeasureable and will be corrupted with a significant direct current component of 10 to 200 microV. For electrophysiological information to be obtained from the abdominal and intrauterine catheter signals, the signals must be in a 0.05 to 100.0 Hz bandwidth. Because typical adult electrocardiogram systems have a noise specification of 5 to 10 microVpp from instrumentation, these systems are unacceptable for obtaining a FECG via the intrauterine catheter or noninvasively. Therefore, custom instrumentation has been developed with a noise specification of approximately 1.5 microVpp in a 0.05 to 100.0 Hz bandwidth. Design details of the custom instrumentation will be presented along with a laptop computer based data acquisition and signal processing system using LabVIEW. In addition, clinical data from the intrauterine catheter and noninvasive abdominal wall are presented to determine the feasibility of obtaining a FECG via the custom instrumentation. Clinical data obtained and documented indicates that after maternal electrocardiogram cancellation, a FECG with a good signal-to-noise ratio can be obtained in a diagnostic bandwidth of 0.05 to 100.0 Hz. Because the diagnostic bandwidth is preserved, electrophysiological information can be determined along with heart rate.