基于小波变换的QRS波群检测

提出了一种基于小波多分辨分析的算法,对心电信号进行特征提取和识别。通过小波变换对常规心电图信号进行分解去噪和特征提取,并利用动态自适应阈值和删除多检点,补偿漏检点对QRS波检测进行优化。实验结果表明该方法在QRS波形不失真的情况下,提高了一部分MIT-BIH数据库信号中QRS波识别的准确率,并且对于较低准确率的心电信号的原因进行了分析。     

基于改进小波变换的QRS特征提取算法研究

心电图数据是分析人体心脏病理的基础,针对心电数据的QRS波形特征提取问题,提出一种基于改进小波变换的特征提取方法.首先,选用小波函数Coiflet对采集信号2层分解,然后进行去噪处理.最大限度地保留心电信号有用特征成分,采用极大值与斜率双重检测法进行QRS波中R峰的定位,进而准确提取QRS特征.最后利用MIT-BIH数...     

一种基于Hilbert变换的R波检测算法

基于希尔波特(Hilbert)变换性质和自适应阈值检测原理,本文提出一种新的心电信号R检测算法。该方法经MIT-BIH心电数据库数据验证,可有效降低基线漂移和高频噪声的干扰,克服高大P波和T波的影响,准确检测率在99．84%以上,算法简单,实时性好。     

基于小波变换和FPGA的心电监测系统设计

为了提高心电系统除噪效果和运算速度,提出一种基于小波变换和FPGA的心电检测系统。利用离散小波快速算法—Mallat算法,简化小波算法的复杂性。并合理选择母小波和阀值的计算方法,提高了重构信号对真实信号的逼近程度。通过FPGA实现小波算法,利用FPGA运算的并行性,提高了系统的运算速度。采用VHDL编写AD和LCD的控制程序,实现了信号采集和显示的功能。经过MIT/BIH数据对算法进行了仿真验证,表明该算法具有良好的除噪效果,其信噪比SNR可达122.6987,均方差MSE可达0.0023。     

小波变换边缘检测特性分析

本文一改以往的以仿真的感性效果作为信号边缘检测质量的效果评价方法，提出小波变换边缘检测定位精度和抗噪声能力量化分析方法，基于小波变换的边缘检测算法，物理意义上是一个先进平滑，再进行边缘检测的过程，其边缘持性与边缘类型和尺度大小有关，随尺度增大，定位偏差增大，反映了小波变换局部化特征强弱对边缘检测特性的影响，本文给出了不含噪声和含有噪声情况下，典型边缘定位精度的量化表述。     

小波变换用于信号突变的检测

本文介绍了小波变换用于信号突变的检测原理，给出了实现小波变换的快速算法。语音的基音检测作为一个应用实例，在文中得到验证。理论与实验表明，利用小波变换检测基音与传统方法相比具有独到之处。     

小波变换用于信号突变点的检测

小波分析的形成是傅里叶分析发展史上里程碑式的进展，小波分析优于傅里叶变换的地方在于它在时域的频域同时具有良好的局部化性质。本文在简单介绍小波变换的基础上，着重介绍了小波变换用于信号突变的检测原理。计算机模拟结果表明，小波变换理论在信号突测中是非常有效的。     

基于小波脊线-Hough变换的LFM信号检测

线性调频(LFM)信号是现代雷达广泛使用的一种大时宽-带宽积的低截获概率信号,根据线性调频信号的小波变换特性,小波脊线与瞬时频率的对应关系,提出了一种检测线性调频信号的联合小波脊线-Hough变换方法,该方法首先计算信号的小波变换,得到二维时-频能量分布图,采用脊算法提取信号的小波脊线,然后在小波脊线时-频平面上再进行Hough变换,从而检测噪声中的线性调频信号并估计信号参数.仿真结果证明,此方法可有效地对线性调频类信号进行检测,并且有较好的抗噪声性能.     

基于小波变换的电磁泄漏信号检测方法

电磁泄漏信号检测已经有好多年的历史,尤其是在电磁兼容学科中,更为重要.然而,电磁信号的不平稳、瞬变、微弱等特点,一直困扰着传统的电磁信号频域检测技术.针对电磁信号频域的这些特点,本文提出了基于小波变换的一种电磁泄漏信号检测技术,并针对实际信号的特点选取了db3作为小波基函数,最后给出了实际检测的结果,以及分析和结论.实际结果表明,该方法可以有效地检测出电磁泄漏信号,为电磁环境检测的智能化作了一个准备.     

小波变换在热波检测图像增强中的应用

为解决红外热波检测图像高噪声,低对比度的问题,将小波变换引入到图像增强环节中。先对小波变换的图像增强原理进行介绍,并对增强过程进行设计。为得到最佳增强效果,对增强函数中的阈值、增强系数的选取进行了研究,并指出,阈值T主要影响降噪效果,增强系数K1主要影响过渡区域的增强效果,增强系数K2主要影响图像的对比度,并对参数的选取方法进行说明。在此基础上,最终选取阈值T为670,K1为10,K2为0.1,对某复合材料的红外热图进行增强处理,通过与原图对比,证明了该方法的有效性。     

Analysis of first-derivative based QRS detection algorithms.

Accurate QRS detection is an important first step for the analysis of heart rate variability. Algorithms based on the differentiated ECG are computationally efficient and hence ideal for real-time analysis of large datasets. Here, we analyze traditional first-derivative based squaring function (Hamilton-Tompkins) and Hilbert transform-based methods for QRS detection and their modifications with improved detection thresholds. On a standard ECG dataset, the Hamilton-Tompkins algorithm had the highest detection accuracy (99.68% sensitivity, 99.63% positive predictivity) but also the largest time error. The modified Hamilton-Tompkins algorithm as well as the Hilbert transform-based algorithms had comparable, though slightly lower, accuracy; yet these automated algorithms present an advantage for real-time applications by avoiding human intervention in threshold determination. The high accuracy of the Hilbert transform-based method compared to detection with the second derivative of the ECG is ascribable to its inherently uniform magnitude spectrum. For all algorithms, detection errors occurred mainly in beats with decreased signal slope, such as wide arrhythmic beats or attenuated beats. For best performance, a combination of the squaring function and Hilbert transform-based algorithms can be applied such that differences in detection will point to abnormalities in the signal that can be further analyzed.     

Integrated approach for fetal QRS detection

Fetal magnetocardiography provides reliable signals of the fetal heart dynamics with high temporal resolution that can be used in a clinical setting. We present a robust Hilbert transform method for extraction of the fetal heart rate. Our method may be applied to signals derived from a single channel or an array of channels. In the case of multichannel data, the channels can be combined to improve signal-to-noise ratio for the extraction of fetal heart data. The method is inherently insensitive to fetal position or movement and, in addition, can be automated. We demonstrate that the determination of R-wave timing is relatively insensitive to waveform morphology. The method can also be applied if the data were preprocessed by independent component analysis (ICA). We compared the Hilbert method, ICA, ICA $+$ Hilbert, and raw signals and found that the Hilbert method gave the best overall performance. We demonstrated that there were approximately 171 errors in 46 789 fetal heart beats.      

Neural-network-based adaptive matched filtering for QRS detection

We have developed an adaptive matched filtering algorithm based upon an artificial neural network (ANN) for QRS detection. We use an ANN adaptive whitening filter to model the lower frequencies of the ECG which are inherently nonlinear and nonstationary. The residual signal which contains mostly higher frequency QRS complex energy is then passed through a linear matched filter to detect the location of the QRS complex. We developed an algorithm to adaptively update the matched filter template from the detected QRS complex in the ECG signal itself so that the template can be customized to an individual subject. This ANN whitening filter is very effective at removing the time-varying, nonlinear noise characteristic of ECG signals. Using this novel approach, the detection rate for a very noisy patient record in the MIT/BIH arrhythmia database is 99.5%, which compares favorably to the 97.5% obtained using a linear adaptive whitening filter and the 96.5% achieved with a bandpass filtering method.     

On the detection of QRS variations in the ECG

Detection of subtle beat-to-beat variations in the morphology of the ECG is complicated by the effects of alignment errors and respiration. A method of directly estimating the alignment error (trigger jitter) from an ECG is derived by relating the variance to the squared slope of the averaged QRS complex. Results based on recordings obtained from 12 normal subjects and alignment performed by the cross-correlation method showed that the alignment errors were dependent upon the choice of the alignment channel, with the best distribution of the errors occurring when alignment was based on the vector magnitude of the three orthogonal leads. The estimated average alignment errors ranged from 0.33-0.42 ms, which were near the optimal value of 0.29 ms based on the sampling rate of 1000 samples/s. It was shown that the effects of respiration could be reduced by normalizing the amplitude of the QRS complexes. It was also estimated that a significant proportion of the variation (0.54-0.67) in the normalized ECG's could be attributed to alignment errors and noise     

QRS slopes for detection and characterization of myocardial ischemia.

In this study, the upward I(US) and downward I(DS) slopes of the QRS complex are proposed as indices for quantifying ischemia-induced electrocardiogram (ECG) changes. Using ECG recordings acquired before and during percutaneous transluminal coronary angioplasty (PTCA), it is found that the QRS slopes are considerably less steep during artery occlusion, in particular for I(DS). With respect to ischemia detection, the slope indices outperform the often used high-frequency index (defined as the root mean square (rms) of the bandpass-filtered QRS signal for the frequency band 150-250 Hz) as the mean relative factors of change are much larger for I(US) and I(DS) than for the high-frequency index (6.9 and 7.3 versus 3.7). The superior performance of the slope indices is equally valid when other frequency bands of the high-frequency index are investigated (the optimum one is found to be 125-175 Hz). Employing a simulation model in which the slopes of a template QRS are altered by different techniques, it is found that the slope changes observed during PTCA are mostly due to a widening of the QRS complex or a decrease of its amplitudes, but not a reduction of its high-frequency content or a combination of this and the previous effects. It is concluded that QRS slope information can be used as an adjunct to the conventional ST segment analysis in the monitoring of myocardial ischemia.     

基于超像素的栅栏检测

栅栏在日常生活中无处不在。当通过栅栏拍摄时,得到的照片因为栅栏的遮挡,丢失了很多纹理信息。文中提出了一种新的基于超像素的栅栏检测方法,从而很好地将栅栏和背景分割开。通过改进的Superpixels算法准确地定位细长的栅栏,并获取栅栏的颜色信息,根据颜色信息提取栅栏。实验结果表明,文中提出的方法能够有效地检测栅栏。     

Combining algorithms in automatic detection of QRS complexes in ECG signals.

QRS complex and specifically R-Peak detection is the crucial first step in every automatic electrocardiogram analysis. Much work has been carried out in this field, using various methods ranging from filtering and threshold methods, through wavelet methods, to neural networks and others. Performance is generally good, but each method has situations where it fails. In this paper, we suggest an approach to automatically combine different QRS complex detection algorithms, here the Pan-Tompkins and wavelet algorithms, to benefit from the strengths of both methods. In particular, we introduce parameters allowing to balance the contribution of the individual algorithms; these parameters are estimated in a data-driven way. Experimental results and analysis are provided on the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia Database. We show that our combination approach outperforms both individual algorithms.     

Nonlinear transforms of ECG signals for digital QRS detection: aquantitative analysis

A class of algorithms has been developed which detects QRS complexes in the electrocardiogram (ECG). The algorithms employ nonlinear transforms derived from multiplication of backward differences (MOBD). The algorithms are evaluated with the American Heart Association ECG database, and comparisons are made with the algorithms reported by Okada (1979) and by Hamilton and Tompkins (1986). The MOBD algorithms provide a good performance tradeoff between accuracy and response time, making this type of algorithm desirable for real-time microprocessor-based implementation     

基于DPM模型的行人检测技术的研究

在行人检测领域,当场景很复杂时,一般行人检测算法往往得不到很好的检测效果。比如在行人很多且靠的很近时,用基于梯度直方图的检测算法时,检测效果不是很好。由P.Felzenszwa提出一种以可变形部件模型为基础的检测算法,能够检测多样变化的目标类型并且在挑战Pascal目标检测中达到较高水平。该算法使用隐变量支持向量机,是一种在支持向量机基础上添加潜在变量而重新构建的支持向量机。本文提出了一种基于可变形部件模型的行人检测算法,通过建立多人体模板,在行人相互靠近有重叠的场景下有着很好的检测效果。