Hilbert-Huang变换应用于心电信号消噪

目的：对心电信号消噪。材料与方法：本文讨论了Hilbert—Huang变换中经验模态分解和瞬时频率求解的两个过程及其原理，着重于把经验模态分解运用到心电信号，将其分解成有限数目的模态函数，对模态函数进行不同的阈值和处理。结果：Hilbert-Huang变换方法可有效地去除心电信号中的一般干扰。结论：与传统的小波变换消噪相比．Hilbert—Huang变换应用于心电消噪有一定的优越性。     

心电信号小波去噪的改进算法研究

对心电信号(ECG)中的基线漂移、工频干扰和肌电干扰等噪声进行去除,在波形识别、医疗诊断和治疗等领域具有重要意义。提出用sym5小波函数对心电信号进行8层小波分解。根据有用信号强度在每一层平均分配而噪声强度随分解层数增加而减少的规律,将分解得到的每一层的小波细节系数设置不同的阈值,最后用所提出的新阈值函数进行小波阈值去噪。该阈值函数既能克服硬阈值函数在阈值附近不连续的缺点,又可弥补软阈值函数与原函数之间存在固定差值的不足。以MIT-BIH心电数据库中的101号文件作为原始数据,将整个数据文件进行平均分段,每段有1 200个数据点,对每段数据进行加噪仿真分析,结果表明所提出的去噪算法得到的去噪信号信噪比比硬阈值函数和软阈值函数分别提高2.31%和8.04%,从而证明所提算法的有效性。     

一种新的长时心电信号模拟方法

心电信号发生器在心电信号相关教学培训和心电医疗设备检测中有着广泛的应用。现有的心电信号发生器通常通过存储于设备内的实测心电信号或者用数学函数波形拟合发生心电信号波形,在逼真度和灵活度上各有缺陷。本研究试图研究一种能综合现有心电信号模拟方法优点的新的模拟方法,使心电信号发生器生成质量更高,更丰富的信号,拓展它的应用范围。通过将单周期心电信号模板库与RR间期模拟信号结合,本研究实现了种类丰富,逼真度高,可自由调节心率特性的心电信号的模拟。这种方法同时还具有运算量小和节省存储空间的特点。     

心电信号数据压缩技术的发展

心电数据压缩是心电信号计算机处理技术的一个重要组成部分，ＥＣＧ压缩算法一般可分成：直接压缩法、变换压缩法和特征参量提取法。文献「１」是篇重要的ＥＣＧ压缩技术综述，归纳了ＥＣＧ压缩原理及在９０年代之前的发展状况；本则简要描述九十年代以来有关ＥＣＧ压缩算法工，主要侧重于周期压缩法，小波变换压缩法和神经网络方法。     

脑电信号的复杂度在睡眠分期中的应用

睡眠既是人体重要的生理状态，也是临床监护重要的一环，按国际通用的睡眠分期方法，必须在脑电信号之外增加脑功能信号，本文研究了单纯的脑电信号的特征———复杂度作睡眠分期的依据的可能性，在数例已按国际通例作了睡眠分期的病例中，应用了本文提出的方法，在对分期结果的比较中提出了需要进一步研究的方面。     

限幅技术在临床心电信号记录中的应用

以心电信息的电生理机制和电压效应理论为基础,探讨如何利用限幅技术实现临床最佳记录效果。经临床试验证明,限幅技术的应用,使得心电生理检查中微弱信号（如希氏束电图中的H波）获得不失真放大,同时被过度放大的信号初始清晰,并有利于波形信号极性确认,而且每导之间互不干扰重叠,此外,在经食管心电生理的记录中,过大的脉冲也因实施了限幅技术而被限制在特定的电平内,不仅明显提高了记录质量,而且也保护了心电记录设备。     

基于Jensen-Shannon Divergence的心电信号复杂度分析

本文采用基于Jensen-Shannon Divergence(JSD)的复杂度分析方法,从MIT-BIH标准数据库中提取正常窦性心率(NSR)、充血性心力衰竭(CHF)和心脏性猝死(SD)信号,分别计算它们的复杂度。结果表明,这三种心电(ECG)信号的复杂度有所不同:NSR信号的复杂度最高,CHF信号次之,SD信号最低。方差检验结果表明,此方法得出的三种ECG信号的复杂度具有显著性差异,对临床医学检测和诊断CHF和SD有很好的借鉴意义。     

基于LabVIEW的心电信号采集系统的设计

目的设计一套基于LabVIEW的心电信号采集系统.方法在插入式信号采集板DAQ的硬件支持下,利用LabVIEW编程软件,设计了一套双通道心电信号采集系统,本系统用传统的心电图机采集心电信号,经过调解后,输入计算机采集并显示,同时,为了能够对心电信号作进一步的研究,利用小波变换对心电信号进行了处理.结果成功采集到心电信号,并计算了心率的大小.     

医用心电信号发生器的设计

随着医学的进步与科学技术的发展,人们对疾病的诊治与健康的保障提出了越来越高的要求.心脏是人体重要的器官,而心脏病已成为危害人类健康的主要疾病之一了,因此心脏系统疾病的防治和诊断是当今医学界面临的首要问题.目前使用的医用心电信号发生器,基本是进口的,价格昂贵.而国内生产的产品由分立元件组成,其体积大,功能少,没有自己的核心技术.基于此开发设计一款医用心电信号发生器.该设计中采用三星公司的ARM芯片S3C2410作为整个系统的主控芯片;包括微控制器、以太网通信接口、串行通信接口、USB通信接口、电源电路、DA转换电路和信号处理电路;综合了电路设计、微机控制技术、通信技术及相应的软件技术等.不但具备普通心电信号发生器的功能,而且可以把心电数据中实际人体的信号还原出来,还可以达到利用标准的心电数据库的心电数据实现不同厂家心电产品的性能对比的目的.可预见其市场前景广大.     

基于小波变换的心电信号去噪处理

人体心电信号随着检测状态及时间的变化具有明显的非平稳性及包含许多干扰的特点.本文将小波变换的时频定位特性运用于心电信号的测量,利用小波变换多尺度多分辨的特点对心电信号进行分解,不同频带的信号便显现在小波分解的不同尺度上.进行信号重构时,去除各种干扰成份,从而获得精确的心电波形,为医疗诊断提供了更加准确的依据.     

时窗复杂度序列在睡眠脑电分期中的应用

运用时窗复杂度序列来分析睡眠脑电，减少了非平稳性及状态空间的不均匀性造成的脑状态信息的丢失，在一定程度上克服了复杂度自身的局限，有助于不同睡眠期状态特征的提取。另外采用独立分量分析（ICA），小波变换等方法对脑电进行预处理，实验表明它们能有效地去除脑电中的一些生理干扰，有利于提高复杂度算法在睡眠分期应用中的精确度。     

基于神经网络的心电数据压缩

针对动态心电监护系统中心电监护数据量很大,其中只有一部分特征才对诊断有意义,本文提出了一种基于神经网络的心电数据压缩算法。该算法根据ECG特征的变化,自动调整神经网络的结构参数,从ECG中提取出对诊断有意义的信息,并具有并行处理、自学习等特点。     

不同睡眠期脑电图复杂性研究

ＥＥＧ代表了大脑神经元活动的一种电信号，它一直是研究人在不同睡眠阶段大脑活动的重要工具。本文在国际睡眠分期标准的基础上，利用Ｌｅｍｐｅｌ－Ｚｉｖ所定义的随机序列的“复杂性”对不同睡眠期的ＥＥＧ时间序列了分析和研究。结果显示，随被试睡眠深度的增加，各道ＥＥＧ的复杂度减少。而在ＲＥＭ期，ＥＥＧ的复杂度又回升到接近清醒、闭眼休息时的程度。这个规律不受是位置的影响。对结果的分析可以看到，复杂度能成为反映民     

EEG复杂性测度用于大脑负荷状态的研究

Ｌｅｍｐｌ－Ｚｉｖ意义下的随机序列的复杂性反映了有限长序列的随机程度。本文研究了大脑不同负荷状态时脑电图的复杂性，分析了大脑负荷状态与相应状态下ＥＥＧ复杂度之间的关系。     

基于黑板模型的心电图解释系统BBIS－ECG设计

作者提出一种基于黑板模型的心电信号解释系统的设计方案，该系统模拟心电专家诊断的思维过程，通过样本学习自动获取模糊规则，以启发性知识控制诊断流程，并通过对模糊量隶属度函数和模糊规则可信度因子的计算，以双向非精确推理进行诊断。同时，将计算模型纳入知识库，应用信号处理的基本原理在信号处理与信号解释之间也建立了双向作用，实现了真正意义上的定性推理与定量计算的结合。并利用同一通道信号的上下文信息及不同通道间的相关信息协助诊断，更有效地利用了数据信息。     

Frequency-plane analysis of normal and pathological ECG signals for disease identification

In this paper a frequency plane analysis of both normal and diseased ECG signals is performed specifically for disease identification. Image processing techniques are used to develop an automated data acquisition package of 12 lead ECG signals from paper records. A regeneration domain is also developed to check the captured pattern with the original wave shape. A QRS complex detector with an accuracy level ～98.4% in up to 30% signal to noise level is developed. Discrete Fourier transform (DFT) is performed to obtain the frequency spectrum of every ECG signal. Some interesting amplitude and phase response properties of chest lead V2, V3, V4, V6 and limb lead I, II, III, AVL, AVF are seen. Both amplitude and phase properties are different for normal and diseased subjects and can serve an important role in disease identification. A statistical analysis of amplitude property is carried out to show that this property is significantly different for normal and diseased subjects.     

Generation of digital time database from paper ECG records and Fourier transform-based analysis for disease identification

ECG signals recorded on paper are transferred to the digital time database with the help of an automated data extraction system developed here. A flatbed scanner is used to form an image database of each 12-lead ECG signal. Those images are then fed into a Pentium PC having a system to extract pixel-to-pixel co-ordinate information to form a raw database with the help of some image processing techniques. These raw data are then ported to the regeneration domain of the system to check the captured pattern with the original wave shape. The sampling period of each ECG signal is computed after detection of QRS complex. Finally, discrete Fourier transform of the generated database is performed to observe the frequency response properties of every ECG signal. Some interesting amplitude properties of monopolar chest lead V4 and V6 are noticed which are stated.     

Selection of effective features for ECG beat recognition based on nonlinear correlations

Objective

The objective of this study is to develop feature selectors based on nonlinear correlations in order to select the most effective and least redundant features from an ECG beat classification system based on higher order statistics of subband components and a feed-forward back-propagation neural network, denoted as HOS-DWT-FFBNN.

Methods and materials

Three correlation-based filters (NCBFs) are proposed. Two of them, NCBF1 and NCBF2, apply feature-feature correlation to remove redundant features prior to the feature selection process based on feature-class correlation. The other, SUFCO, skips the redundancy reduction process and selects features based only on feature-class correlation. The performance of these filters is compared to another commonly used nonlinear feature selection method, Relief-F. The discriminality and redundancy of the retained features are evaluated quantitatively. The performance of the most effective NCBF is compared with that of the linear correlation-based filter (LCBF) and other representative heartbeat classifiers in the literature.

Results

The results demonstrate that the two NCBFs based on both feature-feature and feature-class correlation methods, i.e. NCBF1 and NCBF2, outperform the other two methods, i.e. SUFCO and Relief-F. An accuracy of as high as 96.34% can be attained with as few as eight features. When tested with statistical methods, the retained features selected by the NCBF1/NCBF2 approach are demonstrated to be more discriminative and less redundant when compared with those features selected by other methods. When compared with LCBF and other heartbeat classifiers in the literature, the proposed NCBF1/NCBF2 approach in conjunction with the HOS-DWT-FFBNN structure outperform them with improved performance that allows discrimination of more beat types and fewer feature dimensions.

Conclusion

This study demonstrates the effectiveness and superiority of the proposed approach for ECG beat recognition.     

面向心电信号去噪的正交小波构造方法

为更好地去除心电信号的肌电干扰,设计出专门用于心电信号处理的正交小波(心电小波)。首先,为满足正交性要求,对小波滤波器系数进行参数化设计;其次,用参数化的小波滤波器系数逼近QRS模板并构造出目标函数,在此基础上以消失距作为约束条件得到优化模型;最后,采用拉格朗日乘子法对模型进行求解。从光滑性、与心电信号的相似性、重构误差以及频响特性等方面,对构造出的小波性能进行评估,结果表明,相对于db和sym系列的小波,心电小波可以获得更好的综合性能。当将该算法用于MIT-BIH数据库中的心电信号去噪时,能够抑制加性噪声的干扰,使信噪比提高10.32dB。根据应用对象设计正交小波的方法,为其他专用小波设计提供新思路。     

A method for reduction of power line interference in the ECG

A method for reduction of power line interference (PLI) in electrocardiograms with sampling rate integer multiple of the nominal power line frequency is developed and tested using simulated signals and records from the databases of the American Heart Association and the Massachusetts Institute of Technology. The method involves parabolic detrending of the ECG, estimation of the signal components with frequencies corresponding to PLI by discrete Fourier transform, and minimum-squared-error approximation of decimated series of averaged instantaneous values of PLI using appropriately defined weights. Tests of the proposed method with simulated data show that for signal-to-noise ratios SNR greater than 25 dB, the logarithmic relative error is below −32 dB and the error-to-signal ratio is less than −24 dB. For lower SNR, the errors increased, and a noise attenuation of 0.6 dB is observed. The main advantage of the developed method in comparison with other simpler and faster approaches is the accurate interference reduction in cases when the power line frequency deviates from the nominal 50 or 60 Hz. Due to computational burden, the method is more suitable for off-line application, but real-time implementation, using modern processors, is also possible.