基于快速定点独立分量分析算法的母胎心电信号分离

研究快速定点独立分量分析方法在母胎心电信号分离中的应用。采用此算法,在胎儿心电信号与母体心电信号可以视为相互独立的信号源的前提下,对来源于同一孕妇的观测信号进行独立分量分离。快速定点独立分量算法可以有效地分离出单个独立分量,得到的胎儿心电信号(FECG)较理想。采用独立分量分析方法,实现母胎心电信号分离,是一种值得尝试的信号处理方法。     

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。     

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。     

基于独立成分和小波分析的胎儿心电提取

背景：在胎儿心电信号的采集过程中,会受到母体和其他噪声的强干扰,如何快捷与有效地提取出胎儿心电将成为重要的研究课题。 目的：采用结合独立成分分析和小波分析的方法对来自于同一母体的观测信号进行独立分量分离,得到有效的胎儿心电。 方法：结合独立成分分析和小波分析的算法进行胎儿心电的特征提取,首先对含噪信号进行小波变换,去除奇异信号和非平稳随机信号,然后对小波重构后的信号运用快速独立成分分析算法进行成分分析。 结果与结论：在胎儿心电信号的采集过程中,会受到母体和其他噪声的强干扰,但这些信号都是随机的,不相关的,可以认为它们间是相互独立的。采用结合独立成分和小波分析的方法对来自于同一母体的观测信号进行独立分量分离,得到有效的胎儿心电。实验证明该方法是一种有效的方法。     

基于独立元分析和联合小波熵检测多导联ECG信号的QRS

QRS波群的准确定位是ECG信号自动分析的基础。为提高QRS检测率,提出一种基于独立元分析(ICA)和联合小波熵(CWS)检测多导联ECG信号QRS的算法。ICA算法从滤波后的多导联ECG信号中分离出对应心室活动的独立元;然后对各独立元进行连续小波变换(CWT),重构小波系数的相空间,结合相空间中的QRS信息对独立元排序;最后检测排序后独立元的CWS得到QRS信息。实验对St.Petersburg12导联心率失常数据库及64导联犬心外膜数据库测试,比较本文算法与单导联QRS检测算法和双导联QRS检测算法的性能。结果表明,该文算法的性能最好,检测准确率分别为99.98%和100%。     

基于RLS-ANC自适应滤波的FECG信号提取方法

本文应用RLS-ANC(recursive least squares adaptive noise canceⅡation)自适应滤波方法提取胎儿心电(FECG)信号.该方法采用RLS-ANC自适应滤波消除母亲心电,提取胎儿心电信号.实验结果表明,本方法适应非平稳信号的能力强,收敛速度快,提取效果好于NLMS(normalized least mean squares)算法.     

基于虚拟通道ICA方法的诱发电位单导少次提取

脑电诱发电位的单导少次提取一直是生物医学信号处理领域倍受关注的问题。独立分量分析作为解决盲源分离问题的一种有效算法已被广泛应用于诱发电位提取之中。独立分量分析处理的是多路观测信号,且要求观测信号路数大于或等于独立信号源的个数。为了能够应用独立分量分析算法实现诱发电位的单导少次提取,引入虚拟通道构建观测信号矩阵,从而得到符合实际应用条件的算法模型。4路信号仿真实验表明了虚拟通道模型可以有效提取诱发电位。对12位受试者进行模式翻转视觉诱发电位测试,仅用单导连续4次记录即可实现诱发电位的初步提取,信噪比增加约为12 dB,当采用10路虚拟通道,信噪比提高约20 dB。4路和10路虚拟通道ICA方法下得到的多导联VEP相关系数的统计结果进一步证实增加虚拟通道的数量,EP信号提取效果也会更好。     

独立分量分析在脑电信号处理中的应用及研究进展

独立分量分析(independent component analysis,ICA)方法是从一组观测信号中提取统计独立分量的方法.因为用这种方法分解出的各信号分量之间是相互独立的,而测得的脑电信号往往包含若干相对独立的成分,所以用它来分解脑电信号,所得的结果更具有生理意义,有利于去除干扰和伪差.本文简要地回顾了ICA的发展历史和主要算法,综述了它在脑电信号处理中的应用及研究进展,并指出了需要进一步研究解决的问题.     

用平均法进行胎儿ECG的测量

胎儿心电图(FECG)已广泛得到应用,目前不仅能精确地测量胎儿的心率,而且PR间期、ST段形态和QRS波宽等与胎儿状况有关的参数也受到临床的重视。FECG测量的难点在于信噪比太低,母亲心电(MECG)和FECG同时发生的儿率较大及信号和噪声的频谱混叠在一起。本文介绍了一种用相干函数和FFT算法求得平均的FECG     

独立分量分析技术在EEG工频干扰分离中的应用

工频干扰是脑电图(EEG)中常见噪声，严重影响EEG-信号的提取和分析。通过比较Fastica、Extended Infomax、EGLD、Pearson—ICA等四种独立分量分析(ICA)算法和奇异值分解(SVD)技术用于分离EEG中工频干扰的效果，确证ICA方法有很好的抗干扰性，而常用的SVD技术则难以奏效；其中推广的最大熵(Extended Info—max)ICA算法有较好的收敛性，文中使用该算法成功地从16导联早老性痴呆症患者EEG信号中(含混入的工频干扰，最低信噪比约为0dB)分离出工频干扰。ICA在生物医学信号处理特别是临床医学工程中潜在着重要应用前景和研究价值。     

空间ICA在fMRI数据上的应用与分析

独立成分分析(ICA)技术试图将多维数据分解成若干个相互统计独立的分量。时间ICA和空间ICA都可以用于分析功能核磁共振成像(fMRI)数据。但由于fMRI数据空间维数远远大于时间维数，为计算方便，在分析fMRI数据时。则更多的使用空间ICA方法。本文在单任务激励实验中，利用ICA方法从fMRI数据中分离出若干个与任务相关的独立分量，其中包括与任务相关的恒定分量(CTR)和与任务相关的暂态分量(TTR)；通过将这些独立分量进行空间映射，得到了与任务相关的脑部激活区域。将此结果与SPM的分析比较，得到了一致的结果。在对结果的分析中，我们进一步指出了ICA方法的特点和局限性。     

Multiple neural origins of early auditory evoked potential of rat

The multiple-origin hypothesis has been often considered for an unclear neurogenesis of a characteristic wave in various evoked potentials, none of which has been verified so far. Auditory evoked potential (AEP) in the temporal cortex of rodents has typical slow positive/negative (P1/N1) biphasic waves, which are occasionally associated with an additional 2-4-ms earlier small deflection (P0/N0). Despite previous extensive efforts, P0/N0 deflection is still discussed within the multiple-origin hypothesis. In this historical perspective, we hypothesized that observable AEP is an additive mixture of mutually temporally independent signals from different origins, and that the balance of the mixture impacts on the waveform of AEP. We attempted to verify this hypothesis for the first time by independent component analysis (ICA) of epidurally densely mapped AEPs in the primary auditory cortex of rats. The mapping showed that low amplitude AEPs tended to have more P0/N0 deflections in both pentobarbital- and ketamine/xylazine-anesthesia preparations. ICA of these AEP maps suggested that AEP consisted of at least three independent components and that the deflection appeared when subcortical contribution to AEP was equal to or larger than cortical contribution. In epicranially measured evoked potentials, subcortical and cortical contributions are mixed together because distances from electrodes to cortical sources approximate distances to subcortical sources. In such conditions, e.g. in human scalp-recording experiments or routine clinical screenings, our idea is specifically worth considering for the interpretation of signals.     

Analysis of fMRI data by blind separation of data in a tiny spatial domain into independent temporal component

Independent Component Analysis (ICA) is a promising tool for the analysis of functional magnetic resonance imaging (fMRI) time series. In these studies, mostly assumed is a spatially independent component map of fMRI data (spatial ICA). In this paper, we assume that the temporal courses of the signal and noises are independent within a Tiny spatial domain (temporal ICA). Then with fast-ICA algorithm, spatially neighboring fMRI data were blindly separated into several temporal courses and were preassumed to be formed by a signal time course and several noise time courses where the signal has the largest correlation coefficient with the reference signal. The final functional imaging was completed for the signals obtained from each voxel. Simulations showed that compared with the spatial ICA method, the new temporal ICA method is more effective than the spatial ICA in detecting weak signal in a fMRI dataset. As background noise, the simulations include simulated Gaussian noise and fMRI data without stimulation. Finally, vivo fMRI tests showed that the excited areas evoked by a visual stimuli are mainly in the region of the primary visual cortex and that evoked by auditory stimuli are mainly in the region of the primary temporal cortex.     

Performance comparison of six independent components analysis algorithms for fetal signal extraction from real fMCG data

In this study we compare the performance of six independent components analysis (ICA) algorithms on 16 real fetal magnetocardiographic (fMCG) datasets for the application of extracting the fetal cardiac signal. We also compare the extraction results for real data with the results previously obtained for synthetic data. The six ICA algorithms are FastICA, CubICA, JADE, Infomax, MRMI-SIG and TDSEP. The results obtained using real fMCG data indicate that the FastICA method consistently outperforms the others in regard to separation quality and that the performance of an ICA method that uses temporal information suffers in the presence of noise. These two results confirm the previous results obtained using synthetic fMCG data. There were also two notable differences between the studies based on real and synthetic data. The differences are that all six ICA algorithms are independent of gestational age and sensor dimensionality for synthetic data, but depend on gestational age and sensor dimensionality for real data. It is possible to explain these differences by assuming that the number of point sources needed to completely explain the data is larger than the dimensionality used in the ICA extraction.     

Independent component analysis for synthetic aperture magnetometry in magnetocardiography

We propose independent component analysis (ICA) as a pre-process for synthetic aperture magnetometry (SAM) in magnetocardiogram. SAM is a very useful method for source current imaging. However, SAM cannot separate one source from the others when there are time-correlated multi-sources, especially for successively active sources. The proposed method compensates the intrinsic drawback of SAM with ICA, which is feasible for 3-D imaging of the myocardial current distribution of specific temporal features. By using our method, we successfully localized an accessory pathway of a patient suffering from the WPW syndrome.     

An Improved Fixed-point Algorithm for Independent Component Analysis of Functional MRI Data

The fixed-point algorithm and infomax algorithm are two of the most popular algorithms in independent component analysis （ICA）. However, it is hard to take both stability and speed into consideration in processing functional magnetic resonance imaging （fMRI） data. In this paper, an optimization model for ICA is presented and an improved fixed-point algorithm based on the model is proposed. In the new algorithms a small step size is added to increase the stability. In order to accelerate the convergence, an improvement on Newton method is made, which makes cubic convergence for the new algorithm. Applying the algorithm and two other algorithms to invivo fMRI data, the results show that the new algorithm separates independent components stably, which has faster convergence speed and less computation than the other two algorithms. The algorithm has obvious advantage in processing fMRI signal with huge data.     

新的独立成分分析算法结合主成分分析实现fMRI信号的盲分离

用ICA算法来实现fMRI信号的盲源分离,可以提取出产生fMRI信号的多种源信号。但是在处理过程中存在两个困难:(1)fMRI数据的规模比较大,计算耗时;(2)计算量太大难免产生误差,给结果的分析带来不便。所以我们考虑对数据进行降维,但是如何确定源信号的个数也是一个难题。我们利用信息论的方法来估计源信号的个数,再使用主成分分析对数据进行降维。通过这样的处理,有效地确定了源信号的个数,减少了计算量。然后将一种新的ICA算法(New fixed-point,NewFP)用于处理降维后的数据。最后通过对实际的fMRI信号进行处理,结果表明新算法可以快速有效的分离fMRI信号,且准确性优于FastICA算法。     

A new approach to spike sorting for multi-neuronal activities recorded with a tetrode--how ICA can be practical

Multi-neuronal recording with a tetrode is a powerful technique to reveal neuronal interactions in local circuits. However, it is difficult to detect precise spike timings among closely neighboring neurons because the spike waveforms of individual neurons overlap on the electrode when more than two neurons fire simultaneously. In addition, the spike waveforms of single neurons, especially in the presence of complex spikes, are often non-stationary. These problems limit the ability of ordinary spike sorting to sort multi-neuronal activities recorded using tetrodes into their single-neuron components. Though sorting with independent component analysis (ICA) can solve these problems, it has one serious limitation that the number of separated neurons must be less than the number of electrodes. Using a combination of ICA and the efficiency of ordinary spike sorting technique (k-means clustering), we developed an automatic procedure to solve the spike-overlapping and the non-stationarity problems with no limitation on the number of separated neurons. The results for the procedure applied to real multi-neuronal data demonstrated that some outliers which may be assigned to distinct clusters if ordinary spike-sorting methods were used can be identified as overlapping spikes, and that there are functional connections between a putative pyramidal neuron and its putative dendrite. These findings suggest that the combination of ICA and k-means clustering can provide insights into the precise nature of functional circuits among neurons, i.e. cell assemblies.     

ICA在视觉诱发电位的少次提取与波形分析中的应用

本文提出一种基于扩展的独立分量分析 (ICA)算法的视觉诱发响应少次提取方法。经与目前临床通用的相干平均法比较 ,只经三次平均 ,在波形整体和P10 0潜伏期的提取上 ,效果显著 ,获得医师欢迎 ,很有进一步开发潜力。