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1.
孔令杰 《自动化技术与应用》2014,33(12):35-38
实测的心电信号不可避免地存在一些强干扰和噪声,为了实现准确地提取反映心电信号的特征信息,该文应用一维离散小波变换实现了对心电信号的降噪处理。实验研究结果表明,该方法能够有效地去除心电信号中的噪声,从而为心电信号特征信息的提取奠定了理论基础。 相似文献
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为了方便对患者心电信号进行实时监测,实现对心脏疾病的及时预防及诊断,利用一款基于ATmega328p微控制器的Arduino开发板、一块心电监测前端模块AD8232及上位机软件LabVIEW开发出一套心电实时监测系统,并利用LabVIEW设计出多种软件滤波方法来抑制心电信号中的噪声。由于心电信号的时频特性能提供反映患者心脏活动动态行为的信息,该系统还包括基于LabVIEW设计出的多种用于心电信号实时分析的程序,使被试心电信号所包含的生理特性能够及时地被分析出来。利用所开发的心电实时监测分析系统对被试的心电信号进行采集和分析,发现系统能够非常灵敏、准确地检测心电信号,并对信号噪声有着很好的抑制能力。此外系统能够对信号进行各式的实时分析,且分析结果可靠,能够运用于临床诊断。利用该系统对心电信号进行实时采集和分析,其测量结果准确、去噪效果良好、分析结果可靠,为今后心电实时监测分析系统的设计提供了借鉴。 相似文献
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1 IntroductionH eartdisease is one ofm ain diseases m enace hu-m an' s health. According to relevantinform ation, showin Am erica, Japan and Europe, death rate ofheartdis-ease is the firstin population diseases,hold the positionof third in place in china.… 相似文献
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Electrocardiogram is a signal containing information about the condition and operation of heart. Nowadays, many heart diseases can be efficiently diagnosed using these signals. However, a proper recognition and classification of the heart signals are essential requirement for the diagnosis of heart diseases. In this study, emphasizing on this requirement, a new ECG simulator based on MATLAB Web Figure called WebECG is designed and implemented to facilitate the education on ECG signals. Advanced flexibility and good visualization capabilities including 3-dimension view, zoom and move on ECG graphics are provided by WebECG. The users are able to plot ECG signals with different parameters, to plot the ECGs of nine arrhythmia types. Furthermore, WebECG is capable to add three different noises to ECG and to plot/analyze long-term ECGs. These properties of the WebECG support efficient web-based education of ECG signals. 相似文献
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介绍了一种便携式心电监测仪器,用于监测人心电(ECG)信号.根据获得的心电信号数据,采用小波变换技术进行心电R峰的准确定位,进而得到心率变异(Heart Rate Variability,HRV)信号序列.在对HRV信号进行相空间重构的基础上进行关联维、最大李雅普诺夫指数的估算.结果表明,健康者和心率不齐者的HRV信号的最大李雅普诺夫指数均为正值,但处于心率不齐状态HRV的最大李雅普诺夫指数低于健康状态的最大李雅普诺夫指数. 相似文献
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M. P. S. Chawla 《Neural computing & applications》2009,18(6):539-556
Principal component analysis (PCA) is used for ECG data compression, denoising and decorrelation of noisy and useful ECG components
or signals. In this study, a comparative analysis of independent component analysis (ICA) and PCA for correction of ECG signals
is carried out by removing noise and artifacts from various raw ECG data sets. PCA and ICA scatter plots of various chest
and augmented ECG leads and their combinations are plotted to examine the varying orientations of the heart signal. In order
to qualitatively illustrate the recovery of the shape of the ECG signals with high fidelity using ICA, corrected source signals
and extracted independent components are plotted. In this analysis, it is also investigated if difference between the two
kurtosis coefficients is positive than on each of the respective channels and if we get a super-Gaussian signal, or a sub-Gaussian
signal. The efficacy of the combined PCA–ICA algorithm is verified on six channels V1, V3, V6, AF, AR and AL of 12-channel
ECG data. ICA has been utilized for identifying and for removing noise and artifacts from the ECG signals. ECG signals are
further corrected by using statistical measures after ICA processing. PCA scatter plots of various ECG leads give different
orientations of the same heart information when considered for different combinations of leads by quadrant analysis. The PCA
results have been also obtained for different combinations of ECG leads to find correlations between them and demonstrate
that there is significant improvement in signal quality, i.e., signal-to-noise ratio is improved. In this paper, the noise
sensitivity, specificity and accuracy of the PCA method is evaluated by examining the effect of noise, base-line wander and
their combinations on the characteristics of ECG for classification of true and false peaks. 相似文献
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Elif Derya Übeyli 《Expert Systems》2007,24(2):87-96
Abstract: A new approach based on the computation of a fuzzy similarity index (FSI) is presented for the detection of electrocardiogram (ECG) beats. Four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat) obtained from the Physiobank database were analysed. The ECG signals were decomposed into time–frequency representations using the discrete wavelet transform and wavelet coefficients were calculated to represent the signals. The aim of the study is detection of ECG beats by the combination of wavelet coefficients and the FSI. Toward achieving this aim, fuzzy sets were obtained from the feature sets (wavelet coefficients) of the signals under study. The results demonstrated that the similarity between the fuzzy sets of the studied signals indicated the variabilities in the ECG signals. Thus, the FSI could discriminate the normal beat and the other three types of beats (congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat). 相似文献
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一种改进的心电信号基线漂移矫正方法 总被引:2,自引:0,他引:2
传统滑动平均滤波法有实现容易、计算简单的优点,但在矫正心电信号的基线漂移时容易造成有用心电信号的丢失,从而使滤波后的心电信号产生失真.文中在传统滑动滤波器的基础上,考虑心电数据的采样率并进行跳跃采样对算法进行改进,给出了改进算法的数学模型,并利用MIT-BIH心电数据库中的实测数据对两种算法进行了比较验证.实验表明改进算法处理后得到的结果,在矫正心电信号的基线漂移时与原算法相比,减少了有用心电信号的损失,滤波后的心电信号失真更小,与原始数据的吻合度更高,效果更理想. 相似文献
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Motion artifact removal (MR) is one of the essential issues in processing raw ECG signals since it could not be simply solved by using classic filtering. In this paper, a QRS detection based Motion Artifact Removal algorithm (QRSMR) is proposed. The proposed method detects the entire QRS complex and removes the noise between two QRS complexes, while recovering P and T-waves. As verified in the tests on simulated noisy ECG signals, QRSMR outputs with seriously contaminated ECG signals have an increase of the correlation with their original clean signals from 40% to nearly 80%, demonstrating the improved noise removal ability of QRSMR. Moreover, in the tests on real ECG signals measured on volunteers with a flexible wearable ECG monitoring device developed at Fudan University, QRSMR is able to recover P-wave and T-wave from the contaminated signal, which shows its enhanced performance on motion artifact reduction comparing with adaptive filtering method and other methods based only on empirical mode decomposition. 相似文献
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杜朋戴加飞李锦王俊侯凤贞 《数据采集与处理》2017,32(5):1044-1051
基于非线性格兰杰因果关系分析睡眠生理信号。分别使用多项式核函数、高斯核函数和Sigmoid核函数将低维空间数据映射到高维特征空间,在高维特征空间使用非线性格兰杰因果方法来分析睡眠生理信号。研究结果表明,脑电信号对心电信号的影响比心电信号对脑电信号的影响更为显著,脑电信号对血压信号的影响比血压信号对脑电信号的影响更为显著,血压对心电信号的影响比心电信号对血压信号的影响更为显著,而且睡眠期样本信号间的格兰杰因果关系更为显著。仿真结果验证了睡眠期信号更能客观地反映生理信号的因果关系。 相似文献
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Elif Derya Übeyli 《Neural computing & applications》2009,18(7):653-662
An approach based on the consideration that electrocardiogram (ECG) signals are chaotic signals was presented for automated
diagnosis of electrocardiographic changes. This consideration was tested successfully using the nonlinear dynamics tools,
like the computation of Lyapunov exponents. Multilayer perceptron neural network (MLPNN) architectures were formulated and
used as basis for detection of variabilities of ECG signals. Four types of ECG beats (normal beat, congestive heart failure
beat, ventricular tachyarrhythmia beat, atrial fibrillation beat) obtained from the Physiobank database were classified. The
computed Lyapunov exponents of the ECG signals were used as inputs of the MLPNNs trained with backpropagation, delta-bar-delta,
extended delta-bar-delta, quick propagation, and Levenberg–Marquardt algorithms. The performances of the MLPNN classifiers
were evaluated in terms of classification accuracies. The results confirmed that the MLPNN trained with the Levenberg–Marquardt
algorithm has potential in detecting the variabilities of the ECG signals (total classification accuracy was 95.00%). 相似文献
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In analysing ECG data, the main aim is to differentiate between the signal patterns of healthy subjects and those of individuals with specific heart conditions. We propose an approach for classifying multivariate ECG signals based on discriminant and wavelet analyses. For this purpose we use multiple-scale wavelet variances and wavelet correlations to distinguish between the patterns of multivariate ECG signals based on the variability of the individual components of each ECG signal and on the relationships between every pair of these components. Using the results of other ECG classification studies in the literature as references, we demonstrate that our approach applied to 12-lead ECG signals from a particular database compares favourably. We also demonstrate with real and synthetic ECG data that our approach to classifying multivariate time series out-performs other well-known approaches for classifying multivariate time series. 相似文献
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A syntactic pattern recognition method of electrocardiograms (ECG) is described in which attributed automata are used to execute the analysis of ECG signals. An ECG signal is first encoded into a string of primitives and then attributed automata are used to analyse the string. We have found that we can perform fast and reliable analysis of ECG signals by attributed automata. 相似文献
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Elif Derya Übeyli 《Expert systems with applications》2009,36(5):8758-8767
This paper presented the usage of statistics over the set of the features representing the electrocardiogram (ECG) signals. Since classification is more accurate when the pattern is simplified through representation by important features, feature extraction and selection play an important role in classifying systems such as neural networks. Multilayer perceptron neural network (MLPNN) architectures were formulated and used as basis for detection of variabilities of the ECG signals. Four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat) obtained from the Physiobank database were classified. The selected Lyapunov exponents, wavelet coefficients and the power levels of power spectral density (PSD) values obtained by eigenvector methods of the ECG signals were used as inputs of the MLPNN trained with Levenberg–Marquardt algorithm. The classification results confirmed that the proposed MLPNN has potential in detecting the variabilities of the ECG signals. 相似文献
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利用盲分离技术从母亲腹心电中分离出胎心电在胎心电幅度较强的情况下是可行的,但如果胎心电过弱,盲分离中容易将胎心电视作噪声而无法正确分离.在胎心电过弱时,先对腹心电进行形态学滤波后检测胎心电的R峰,然后在配准胎儿R峰的前提下,平移、叠加并重构信号,最后对重构信号应用盲分离方法分离出较好的胎心电信号.实验证明,当胎心电微弱,直接盲分离容易将胎心电作为噪声而无法得到有效胎心电时,R峰配准重构可以有效地增强胎心电的信号强度,对重构后的信号进行盲分离可得到有效的胎心电,进而得到较精确的胎心率. 相似文献