基于独立元分析和非线性指数分析的脑电信号中伪迹分量的自动去除

脑电(electroencephalography,EEG)信号中不可避免地存在着眼动、心跳、肌电信号以及线性噪声等伪迹干扰,这些伪迹的存在极大地影响了脑电信号分析的准确性,因此在进行脑电信号分析前需要去除伪迹干扰。为了有效地去除伪迹,结合独立元分析和非线性指数分析,提出一种自动识别并去除脑电信号中伪迹分量的方法。该方法还可同时用于提取脑电信号中的基本节律如!波等。相应的模拟与实际脑电数据的实验结果表明所提议的方法具有很好的识别和去除脑电信号伪迹分量的性能。     

多通道脑电信号的盲分离

提出一种新的多通道脑电信号盲分离的方法,将小波变换和独立分量分析(independent component analysis,ICA)相结合,利用小波变换的滤噪作用,将混合在原始脑电的部分高频噪声滤除后,再重构原始脑电作为ICA的输入信号,有效地克服了现有ICA算法不能区分噪声的缺陷。实验结果表明,该方法对多通道脑电的盲分离是很有效的。     

基于独立元分析的MEG(脑磁图)数据分析和处理

独立元分析(independent component analysis,ICA)可用于分离混迭的MEG(Magnetoencephalography)多通道信号中的信号源。从ICA分解的结果中消除干扰源和噪声,并将剩余分量投影回MEG多通道数据空间,可得到净化的MEG信号,表示各个信号源的各独立元分别投影回多通道,可对各活动源进行空间定位。特别是,响应于外界刺激的诱发活动源亦可从重叠的MEG多通道信号中得到分离,这对脑功能研究及脑医学临床应用极有吸引力。提出了一个简单有效的基于ICA的MEG数据分析和处理方法,研究和分析了一些实际应用问题,特别是给出了听觉诱发响应的一些有意义的分析结果。     

空间独立成分分析实现fMRI信号的盲分离

独立成分分析（ICA）在功能核磁共振成像（fMRI)技术中的应用是近年来人们关注的一个热点。简要介绍了空间独立成分分析（SICA）的模型和方法,将fMRI信号分析看作是一种盲源分离问题,用快速算法实现fMRI信号的盲源分离。对fMRI信号的研究大多是在假定已知事件相关时间过程曲线的情况下,利用相关性分析得到脑的激活区域。在不清楚有哪几种因素对fMRI信号有贡献、也不清楚其时间过程曲线的情况下,用SICA可以对fMRI信号进行盲源分离,提取不同独立成分得到任务相关成分、头动成分、瞬时任务相关成分、噪声干扰、以及其它产生fMRI信号的多种源信号。     

基于参考信号的ICA方法进行诱发电位单导提取

诱发电位（EP）信号的检测与分析技术是临床医学诊断神经系统损伤及病变的重要手段之一,但是EP信号总是淹没在人体自发产生的脑电图信号（EEG）中。因此,为利用EP信号诊断神经系统的损伤和病变,本文使用带参考信号的独立分量分析（ICA）方法从混合信号中快速将EP信号提取出来。计算机模拟表明,采用带参考信号的ICA方法可以从单导混合信号中有效地将EP信号提取出来。     

不同思维作业电信号的高准确度模式分类

本文采用独立分量(ICA)分析对不同思维作业的脑电(EEG)信号进行预处理,再用自回归(AR)参数模型提取EEG信号特征,最后利用BP网络完成对特征样本集的训练和分类。实验结果表明,所采用的方法提高了脑电思维模式作业的准确度,对两种到五种不同思维作业未经训练的数据的平均分类准确度达到79％以上,超过现有文献报道的结果。     

一种独立分量分析的迭代算法和实验结果

介绍盲信源分离中一种独立分量分析方法,基于信息论原理,给出了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,得出一种用于独立分量分析的迭代算法。相对于其他大多数独立分量分析方法来说,该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。通过脑电信号和其他信号的计算机仿真和实验结果表明了算法的有效性。     

新的独立成分分析算法实现功能磁共振成像信号的盲分离

采用独立成分分析(independent component analysis,ICA)的一种新的牛顿型算法来提取功能磁共振成像(functional magnetic rasonance imaging,fMRI)信号中的各种独立成分(包括与实验设计相关的成分以及各种噪声)。与fastICA相比,该算法减少了运算量,提高了运算速度,而且能够很好地分离出各个独立成分。结果表明该算法是一种有效的fMRI信号分析手段。     

基于小波变换心电信号消噪方法研究

小波变换是近年来兴起的热门信号处理技术,是一种非常有用的信号处理工具。本文阐述了连续小波去噪和离散小波去噪的原理,分析了基于小波去噪的几种不同方法（其中包括小波分解与重构,小波变换阈值法,小波变换模极大值法,以及它与独立分量分析相结合去除噪声的方法等）。通过检测和验证,表明该方法能较好的实现心电信号的消噪,都取得了较好的效果;同时,比较了每种方法的不足和缺陷。基于小波变换心电信号消噪的研究进展较快,通过多种方法结合运用进行消噪并取得了很好的效果,展望了利用基于小波变换心电信号消噪的前景。     

基于Flexible ICA的睡眠EEG及EOG中心电伪差的消除

提出了一种采用自适应非线性函数的ICA学习算法,Flexible ICA算法,并将其应用于睡眠EEG自动分期的前期预处理中,用于消除采集到的各通道信号中的心电伪差.实验结果证明,Flexible ICA算法能够快速有效的消除各通道的心电伪差,为后期的睡眠EEG自动分期打下了良好的基础.     

Automatic signal classification in fluorescence in situ hybridization images

BACKGROUND: Previous systems for dot (signal) counting in fluorescence in situ hybridization (FISH) images have relied on an auto-focusing method for obtaining a clearly defined image. Because signals are distributed in three dimensions within the nucleus and artifacts such as debris and background fluorescence can attract the focusing method, valid signals can be left unfocused or unseen. This leads to dot counting errors, which increase with the number of probes. METHODS: The approach described here dispenses with auto-focusing, and instead relies on a neural network (NN) classifier that discriminates between in and out-of-focus images taken at different focal planes of the same field of view. Discrimination is performed by the NN, which classifies signals of each image as valid data or artifacts (due to out of focusing). The image that contains no artifacts is the in-focus image selected for dot count proportion estimation. RESULTS: Using an NN classifier and a set of features to represent signals improves upon previous discrimination schemes that are based on nonadaptable decision boundaries and single-feature signal representation. Moreover, the classifier is not limited by the number of probes. Three classification strategies, two of them hierarchical, have been examined and found to achieve each between 83% and 87% accuracy on unseen data. Screening, while performing dot counting, of in and out-of-focus images based on signal classification suggests an accurate and efficient alternative to that obtained using an auto-focusing mechanism.     

Empirical mode decomposition based filtering techniques for power line interference reduction in electrocardiogram using various adaptive structures and subtraction methods

Electrocardiogram (ECG) is a vital sign monitoring measurement of the cardiac activity. One of the main problems in biomedical signals like electrocardiogram is the separation of the desired signal from noises caused by power line interference, muscle artifacts, baseline wandering and electrode artifacts. Different types of digital filters are used to separate signal components from unwanted frequency ranges. Adaptive filter is one of the primary methods to filter, because it does not need the signal statistic characteristics. In contrast with Fourier analysis and wavelet methods, a new technique called EMD, a fully data-driven technique is used. It is an adaptive method well suited to analyze biomedical signals. This paper foregrounds an empirical mode decomposition based two-weight adaptive filter structure to eliminate the power line interference in ECG signals. This paper proposes four possible methods and each have less computational complexity compared to other methods. These methods of filtering are fully a signal-dependent approach with adaptive nature, and hence it is best suited for denoising applications. Compared to other proposed methods, EMD based direct subtraction method gives better SNR irrespective of the level of noises.     

Detection of cerebral embolism using transcranial Doppler sonography: can artifacts be reliably recognized?]

A requirement for the use of TCD for the detection of emboli in the field of cardiac and vascular surgery is the reliable differentiation between true emboli and artifacts. In ten healthy volunteers we carried out a study to establish the method with which artefacts can most reliably be identified. Automatic detection of increasing signal intensity misinterpreted 14% of all artifacts as emboli; 1.7% of all artifacts sounded suspicious for embolism, and 0.6% met the classical criteria of an embolus. Using simultaneous recording of the flow signal in two sections of the middle cerebral artery, all artifacts were identified on the basis of their simultaneous manifestation. Reliable intra-operative differentiation of emboli from artifacts requires attentive, continuous acoustic and visual analysis of signals by an experienced investigator familiar with the surgical procedure. The introduction of a multiple-depth algorithm might significantly improve the automatic detection program.     

A wavelet-based adaptive filter for removing ECG interference in EMGdi signals

Diaphragmatic electromyogram (EMGdi) signals convey important information on respiratory diseases. In this paper, an adaptive filter for removing the electrocardiographic (ECG) interference in EMGdi signals based on wavelet theory is proposed. Power spectrum analysis was performed to evaluate the proposed method. Simulation results show that the power spectral density (PSD) of the extracted EMGdi signal from an ECG corrupted signal is within 1.92% average error relative to the original EMGdi signal. Testing on clinical EMGdi data confirm that this method is also efficient in removing ECG artifacts from the corrupted clinical EMGdi signal.     

Selecting EEG components using time series analysis in brain death diagnosis

In diagnosis of brain death for human organ transplant, EEG (electroencephalogram) must be flat to conclude the patient’s brain death but it has been reported that the flat EEG test is sometimes difficult due to artifacts such as the contamination from the power supply and ECG (electrocardiogram, the signal from the heartbeat). ICA (independent component analysis) is an effective signal processing method that can separate such artifacts from the EEG signals. Applying ICA to EEG channels, we obtain several separated components among which some correspond to the brain activities while others contain artifacts. This paper aims at automatic selection of the separated components based on time series analysis. In the flat EEG test in brain death diagnosis, such automatic component selection is helpful.     

Filtering the surface EMG signal: Movement artifact and baseline noise contamination

The surface electromyographic (sEMG) signal that originates in the muscle is inevitably contaminated by various noise signals or artifacts that originate at the skin-electrode interface, in the electronics that amplifies the signals, and in external sources. Modern technology is substantially immune to some of these noises, but not to the baseline noise and the movement artifact noise. These noise sources have frequency spectra that contaminate the low-frequency part of the sEMG frequency spectrum. There are many factors which must be taken into consideration when determining the appropriate filter specifications to remove these artifacts; they include the muscle tested and type of contraction, the sensor configuration, and specific noise source. The band-pass determination is always a compromise between (a) reducing noise and artifact contamination, and (b) preserving the desired information from the sEMG signal. This study was designed to investigate the effects of mechanical perturbations and noise that are typically encountered during sEMG recordings in clinical and related applications. The analysis established the relationship between the attenuation rates of the movement artifact and the sEMG signal as a function of the filter band pass. When this relationship is combined with other considerations related to the informational content of the signal, the signal distortion of filters, and the kinds of artifacts evaluated in this study, a Butterworth filter with a corner frequency of 20 Hz and a slope of 12 dB/oct is recommended for general use. The results of this study are relevant to biomechanical and clinical applications where the measurements of body dynamics and kinematics may include artifact sources.     

Surface electromyography as a tool to assess the responses of car passengers to lateral accelerations: Part I. Extraction of relevant muscular activities from noisy recordings

The aim of this paper is to develop a method to extract relevant activities from surface electromyography (SEMG) recordings under difficult experimental conditions with a poor signal to noise ratio. High amplitude artifacts, the QRS complex, low frequency noise and white noise significantly alter EMG characteristics. The CEM algorithm proved to be useful for segmentation of SEMG signals into high amplitude artifacts (HAA), phasic activity (PA) and background postural activity (BA) classes. This segmentation was performed on signal energy, with classes belonging to a χ² distribution. Ninety-five percent of HAA events and 96.25% of BA events were detected, and the remaining noise was then identified using AR modeling, a classification based upon the position of the coordinates of the pole of highest module. This method eliminated 91.5% of noise and misclassified only 3.3% of EMG events when applied to SEMG recorded on passengers subjected to lateral accelerations.     

ECG Enhancement and QRS Detection Based on Sparse Derivatives

Electrocardiography (ECG) signals are often contaminated by various kinds of noise or artifacts, for example, morphological changes due to motion artifact, non-stationary noise due to muscular contraction (EMG), etc. Some of these contaminations severely affect the usefulness of ECG signals, especially when computer aided algorithms are utilized. In this paper, a novel ECG enhancement algorithm is proposed based on sparse derivatives. By solving a convex ?₁ optimization problem, artifacts are reduced by modeling the clean ECG signal as a sum of two signals whose second and third-order derivatives (differences) are sparse respectively. The algorithm is applied to a QRS detection system and validated using the MIT-BIH Arrhythmia database (109,452 anotations), resulting a sensitivity of Se = 99.87% and a positive prediction of +P = 99.88%.     

New Respiratory Inductive Plethysmography (RIP) Method for Evaluating Ventilatory Adaptation during Mild Physical Activities

The pneumotachometer is currently the most accepted device to measure tidal breathing, however, it requires the use of a mouthpiece and thus alteration of spontaneous ventilation is implied. Respiratory inductive plethysmography (RIP), which includes two belts, one thoracic and one abdominal, is able to determine spontaneous tidal breathing without the use of a facemask or mouthpiece, however, there are a number of as yet unresolved issues. In this study we aimed to describe and validate a new RIP method, relying on a combination of thoracic RIP and nasal pressure signals taking into account that exercise-induced body movements can easily contaminate RIP thoracic signals by generating tissue motion artifacts. A custom-made time domain algorithm that relies on the elimination of low amplitude artifacts was applied to the raw thoracic RIP signal. Determining this tidal ventilation allowed comparisons between the RIP signal and simultaneously-recorded airflow signals from a calibrated pneumotachometer (PT). We assessed 206 comparisons from 30 volunteers who were asked to breathe spontaneously at rest and during walking on the spot. Comparisons between RIP signals processed by our algorithm and PT showed highly significant correlations for tidal volume (Vt), inspiratory (Ti) and expiratory times (Te). Moreover, bias calculated using the Bland and Altman method were reasonably low for Vt and Ti (0.04 L and 0.02 s, respectively), and acceptable for Te (<0.1 s) and the intercept from regression relationships (0.01 L, 0.06 s, 0.17 s respectively). The Ti/Ttot and Vt/Ti ratios obtained with the two methods were also statistically correlated. We conclude that our methodology (filtering by our algorithm and calibrating with our calibration procedure) for thoracic RIP renders this technique sufficiently accurate to evaluate tidal ventilation variation at rest and during mild to moderate physical activity.     

Comparison of mid-infrared Fourier transform vibrational circular dichroism measurements with single and dual polarization modulations

Fourier transform infrared vibrational circular dichroism (FTIR-VCD) measurements have gone through major advances in the last decade. A major thrust in these advances was to find ways that can minimize the VCD spectral artifacts and obtain the VCD signals at enhanced signal quality. However, all these advances are not incorporated in FTIR-VCD instruments manufactured by some commercial manufacturers. In this article, we compare the measurements obtained with single and dual polarization modulations to seek the attention of the users of single polarization modulation method in minimizing the artifacts in such measurements. We also report that the VCD spectrum of a home-made collagen film can serve as a simple and convenient sample for routine verification of the correct functioning of the VCD spectrometers.