基于空间约束的时间独立成分分析方法分析fMRI数据

首先采用相关分析初步检测可能的功能激活区域,并以初步检测的功能激活区域作为空间约束条件,对fMRI数据进行时间模式的独立成分分析,然后利用功能实验设计时序信息,通过典型相关分析方法对独立成分排序,自动识别与功能实验设计相关的功能信号成分,最后以识别的功能信号成分作为参考函数,重新利用相关分析自适应地分析fMRI数据。通过对实际的fMRI数据分析验证了提出方法的有效性及可靠性。     

ROC分析及其在fMRI信号中的一个应用

详细阐述了受试者工作特征（Receiver operating characteristic，ROC）分析的基本原理，分析了它与其它的诊断评价性标准相比存在的优点，并给出了ROC曲线的绘制方法，最后采用ROC对独立成分分析（Independent component analysis，ICA）和统计参数图（Statistical parametric mapping，SPM）处理功能磁共振成像（Functional magnetic reasonance imaging，fMRI）数据的结果进行了比较。     

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。     

ICA在心音信号处理中的应用

独立成分分析(ICA)是近年来涌现的用于盲信号分离的新技术.本研究利用独立成分分析成功地把心音信号分离为三个独立的成分.借助ICA方法我们能够有效地区分正常人和房间隔缺损病人的心音信号.研究中所用的心音信号由自行研发的心音采集仪所采集.文章首先介绍了独立成分分析的基本原理,然后介绍了一种基于四阶统计量的算法-快速定点算法,并给出了利用此算法的ICA步骤.试验结果表明,ICA在心音信号的处理中是一种很有潜力的方法.     

ICA在肠鸣音信号处理中的应用研究

目的:肠鸣音的去噪及时域特征提取.方法:本文首先分析了独立分量分析的基本原理,研究了FastICA算法,并给出了此算法的具体实现步骤.然后利用该方法对肠鸣音进行了处理,并用归一化平均香农能量分布提取了肠鸣音的时域特征.结果:上述方法有效地去除了肠鸣音信号中的噪声,发现正常音与异常音的时域特征存在典型差异.结论:肠呜音的检测和处理在胃肠道疾病的诊断治疗中具有重要的价值,实验结果可以看出独立分量分析在肠鸣音信号处理中是非常有效的.     

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。     

基于主成分分析和重叠直方图统计的视频信号心率测量

光电容积脉搏波描记法(PPG)原理指出,特定波长的光强度在血液容积作用下呈现周期性变化,因此人面部的肤色会随着脉搏的波动发生微小的变化。根据该原理,可以实现低成本、无接触、无电极的舒适的心率测量,对于远程医疗监控等领域有很大的意义。使用普通摄像头采集人脸视频,通过主成分分析(PCA)对人脸的3个区域的绿色分量的均值提取主成分信号,消除部分外界噪声的干扰;提取的主成分信号经平滑处理后,进行去趋势化处理,然后经过所提出的波峰检测方法,提取其波峰,减少波峰的漏检或多检;此外,采用重叠直方图统计的方法,计算心率信号的波峰间隔。在实验中,采用康泰CMS-50D指夹式脉搏血氧仪(测量样本数174个)和美国BIOPAC公司MP150多导生理记录仪记录的ECG(样本数110个),分别与所提取结果通过Bland- Altman分析方法进行对比。该方法与指夹式脉搏血氧仪测量结果的对比实验表明:叠加直方图统计方法的引入,使一致性评估评估的标准差由12.4 次/min减小到8.0 次/min左右,提高约1/3;与ECG的一致性评估的结果均值为1.8次/min,标准差为3.2 次/min,两者均较小,说明两者结果具有很好的一致性。     

一种独立成分分析的鲁棒算法及其在脑磁图数据分析中的应用

独立成分分析是一种新的信号处理统计方法。被广泛用于各个领域。在信号分析中面临的难题是：源信号的不同特性（既包括超高斯信号又包括亚高斯信号）；未知的独立源数目；传感器信号受到较大的加性噪声污染。针对以上难题，本文提出了一种独立成分分析的鲁棒算法。该方法先对观测数据作预处理，将包含噪声的高维传感器观测信号降维分解到信号子空间和噪声子空间。利用交叉验证法估计出独立源的数目（解决了独立成分分析本身不能确定源数目的缺陷）；然后利用快速稳定的FastICA算法分离独立成分。通过人工合成的数据和实际的脑磁图数据分析。验证了这种方法的功效。     

基于空间独立成分分析的内源性光学成像信号提取

目的：内源信号光学成像分析方法通常使用空白本底或交混本底作为基准图像,然后将刺激条件下获得的皮层图像与基准图像相除或者相减,得到刺激条件下的皮层功能图。但是空白本底和交混本底本身是含有皮层活动信息的,二者也存在结构上的差异,说明本底方法本身可能会带来计算误差。本文的目的是探索一种不使用本底基准图像的内源信号分析方法。方法：将主成分分析与空间独立成分分析相结合,不使用本底相减方法,以自行记录的麻醉猫初级视皮层对正弦光栅刺激反应所得到的成像图像为数据源,直接提取方位功能图。结果：我们提出的基于空间独立成分分析的内源信号分析方法同样可以提取出效果较好的方位功能图。将我们得到的方位功能图与传统本底方法得到的方位功能图的轮廓进行比较,两种分析方法得到的功能构筑信息基本相同。结论：我们提出的基于空间独立成分分析的内源信号提取方法可以有效直接从原始图像提取出方位功能图。     

家庭远程医疗监护数据的传输及处理

介绍了家庭远程医疗监护系统，利用VB编程通过Intemet实现了监护数据的远程传输，医疗中心对接收到的监护数据可以进行去噪、去干扰、特征提取等预处理。在此基础上，实现脑电信号的远程传输，并对医疗中心接收到的脑电信号进行独立成分分析(ICA)分离眼动干扰的处理，取得了较好的效果。     

一种基于独立成份分析的fMRI时-空模型数据处理方法

独立成份分析(ICA)是信号处理领域中斯近发展起来的一种很有应用前景的方法，而脑功能磁共振(fMRI)信号的有效分离与识别是一个正在研究和试验之中的技术领域。因此，发展基于ICA的fMRI数据处理方法具有明显的理论价值和应用前景。本文首先介绍了ICA原理，分析了现行ICA—fMRI方法采用的信号与噪声的空域分布相互独立的信号模型所存在的明显不足，然后提出了微域中的信号与噪声的时域过程相互独立的fMRI信号模型，从而建立了一种新的fMRI数据处理方法：邻域独立成份相关法。合理的fMRI实验数据处理结果验证了新方法的合理性。     

An fMRI study of acupuncture using independent component analysis

In this paper, we studied the brain functional networks corresponding to the traditional multiple-block acupuncture task paradigm. Due to the complexity and sustainability seen during acupuncture, we wanted to investigate whether or not the effects during acupuncture are changing according to the multiple-block paradigm. We introduced the data driven method of independent component analysis (ICA) to identify brain functional networks activated during the course of acupuncture and to isolate different networks likely related to different aspects of the acupuncture experience. The comparisons between different resting states disclosed the discrepancies between the pre- and post-needling effects in the brain. Furthermore, the distinction between needle stimulation and the resting state indicated that there existed different functional brain networks. These results also portray time variability during the course of acupuncture.     

独立成分分析在生物医学信号处理中的应用

独立成分分析(independentcomponentanalysis熏ICA)已经成功地应用到生物医学信号处理中,并被证明是一种分析生物医学信号的强有力的工具,近年来一直受到国内外学者的广泛关注。本文系统地介绍了独立成分分析在生物医学信号(EEG,MEG,fMRI)处理中的应用,分析了其应用方法,最后简要地探讨了独立成分分析应用到生物医学信号中的优势及存在的一些不足。     

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.     

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。     

独立分量分析及其在生物医学工程中的应用

:独立分量分析 ( Independent Component Analysis,简记 ICA)是信号分解技术的新发展。ICA与 PCA(主分量分析 )或 SVD(奇异值分解 )的主要不同是 :后者分解得的各分量只是互不相关 ,而前者则要求各分量相互统计独立。体表测量得的信号往往包含若干相对独立的成分 ,因此采用ICA技术来分解 ,所得结果往往更有生理意义 ,有利于去除干扰和伪迹。本文简短地回顾 ICA的基本原理、判据、算法和其在生物医学工程中的应用 ,并作出展望及指出存在问题。     

A hybrid method of application of independent component analysis to in vivo 1H MR spectra of childhood brain tumours

Independent component analysis (ICA) can automatically extract individual metabolite, macromolecular and lipid (MMLip) components from a series of in vivo MR spectra. The traditional feature extraction (FE)-based ICA approach is limited, in that a large sample size is required and a combination of metabolite and MMLip components can appear in the same independent component. The alternative ICA approach, based on blind source separation (BSS), is weak when dealing with overlapping peaks. Combining the advantages of both BSS and FE methods may lead to better results. Thus, we propose an ICA approach involving a hybrid of the BSS and FE techniques for the automated decomposition of a series of MR spectra. Experiments were performed on synthesised and patient in vivo childhood brain tumour MR spectra datasets. The hybrid ICA method showed an improvement in the decomposition ability compared with BSS-ICA or FE-ICA, with an increased correlation between the independent components and simulated metabolite and MMLip signals. Furthermore, we were able to automatically extract metabolites from the patient MR spectra dataset that were not in commonly used basis sets (e.g. guanidinoacetate).     

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.     

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。     

A comparative study of feature extraction and blind source separation of independent component analysis (ICA) on childhood brain tumour 1H magnetic resonance spectra

Independent component analysis (ICA) has the potential of determining automatically the metabolite signals which make up MR spectra. However, the realiability with which this is accomplished and the optimal approach for investigating in vivo MRS have not been determined. Furthermore, the properties of ICA in brain tumour MRS with respect to dataset size and data quality have not been systematically explored. The two common techniques for applying ICA, blind source separation (BSS) and feature extraction (FE) were examined in this study using simulated data and the findings confirmed on patient data. Short echo time (TE 30 ms), low and high field (1.5 and 3 T) in vivo brain tumour MR spectra of childhood astrocytoma, ependymoma and medulloblastoma were generated by using a quantum mechanical simulator with ten metabolite and lipid components. Patient data (TE 30 ms, 1.5 T) were acquired from children with brain tumours. ICA of simulated data shows that individual metabolite components can be extracted from a set of MRS data. The BSS method generates independent components with a closer correlation to the original metabolite and lipid components than the FE method when the number of spectra in the dataset is small. The experiments also show that stable results are achieved with 300 MRS at an SNR equal to 10. The FE method is relatively insensitive to different ranges of full width at half maximum (FWHM) (from 0 to 3 Hz), whereas the BSS method degrades on increasing the range of FWHM. The peak frequency variations do not affect the results within the range of ±0.08 ppm for the FE method, and ±0.05 ppm for the BSS method. When the methods were applied to the patient dataset, results consistent with the synthesized experiments were obtained. Copyright © 2009 John Wiley & Sons, Ltd.