DWA：一种新的心电实时检测算法

提出了一种简单可靠的心电信号实时检测方法——差分窗口法（DWA）。该算法首先用基线一相对幅值一差分法检测出QRS波群,再采用均值-方差-窗口法对TP波进行检测。试验采用MIT—BIHArrhythmiaDatabase中提供的48组心电数据及一些临床采集的心电数据,先在MATLAB下仿真了DWA算法,然后在ARM7TDMI—S上运行该算法,准确率达99．98％,实现了对心电的实时分析,验证了算法的可行性和可靠性。试验得到的结论为DWA算法能对心电信号的主要特征参数进行提取和识别,从而能够快速准确地对心电图进行诊断,易于在便携式系统中实现。     

心电信号降噪及R波提取技术研究

心电信号中R波的准确定位是其他波形定位的基础,在心电信号特征波形的研究中处于首要位置。本文根据R波在整个心电波形中幅值最为明显的特点,提出了一种基于小波理论提取R波的方法。首先选择合适的方法去除心电信号中的噪声,其次对去噪后的心电信号做5层小波分解得到各阶近似信号与细节信号,再次将得到的第3、4阶细节信号叠加,将叠加信号的均值作为搜索的阈值,并确定搜索起点,最后将搜索起点的横坐标映射至纯净的心电信号中,并使用逐点比对幅值的方式向后搜索,直至找到R波。使用MIT-BIH数据库中的数据进行多次实验验证,实验结果表明此算法能够准确的定位R波,检验的准确率为99.65%,召回率为99.86%。     

基于三次B-样条小波自适应阈值的QRS波检测

小波变换已经广泛应用于QRS波的检测,并达到了很高的检测率。本文针对目前检测方法的不足,选取了滤波器个数较少的三次样条小波作为小波基并结合自适应阈值的方法检测模极大值点以提高检测速度。在MITBIH数据库中验证,表明该方法能有效检出含有严重基线漂移和工频干扰的心电信号,检测率达到99．8％以上。检测速度也优于多种检测方法,有良好的实时效果。     

电压闪变调幅波提取方法的改进

电压闪变是电能质量的重要指标之一,而闪变测量首先需要提取闪变调幅波.提出使用改进的Teager-Kaiser能量算子方法提取闪变调幅波,该方法只需对检测信号的3个采样点进行简单的基本运算,使得提取闪变调幅波的过程快速、简洁.同时,为减少白噪声对算法检测精确度的影响,提出了改进的小波阈值去噪方法,对采集的电信号进行去噪,并通过仿真试验证明所提方法能够准确有效地检测电压闪变的调幅波.该方法克服噪声对电能质量检测的影响,并具有较高精度,易于实现.     

短路电流实时计算的递推最小二乘校正算法

为了缩短实时、准确获取短路电流参数所需要的时间,提出了递推最小二乘校正新算法。该算法利用三个不同的采样起点通过校正的方法来消除衰减分量对计算结果的影响,同时利用递推原理减少计算量,从而实现对短路电流的实时计算。通过仿真校验表明,对于50Hz的电网,当每周波采样64次,信噪比为30dB时,递推最小二乘校正算法最长能够在短路发生后8ms实时、快速、准确地计算出短路电流的基波参数。     

智能手机上心电信号R波的检测

选用墨西哥帽小波变换在智能手机上对心电数据进行预处理,并采用时-频域结合特征点位置信息的方法对R波进行检测.针对传统阈值法在R波检测中存在的一些问题进行改进,提出使用对频域数据进行一阶差分取绝对值,通过求极值的方法来检测倒置R波.通过使用MIT-BIH标准心律失常数据库的心电信号数据做为样本数据进行实验,实验表明该算法能够准确检测R波的特征值,最后将该算法应用到Android智能手机,效果令人满意.     

基于ST损伤向量的心肌缺血罗盘显示

为了将心肌缺血的信息可视化,提出了一种基于ST段损伤向量罗盘显示的心肌缺血检测方法。该方法用ECG设备采集十二导联心电信号,计算各导联的偏移值,用最小二乘解矛盾方程组的方法逆投影到罗盘上,得到ST损伤向量,该向量的大小代表缺血的严重程度,方向指向缺血部位,并用紫色标记。实验对32位心肌缺血患者做检测,将其结果与心电图检测法进行对比,Kappa值为0.903 6,敏感性96.2%,特异性100%,准确性96.9%,阳性率81.3%,P>0.1;对比冠状动脉造影法,Kappa值为0.7949,敏感性96.2%,特异性83.3%,准确性93.8%,阳性率81.2%,P>0.1。数据表明该方法和这两种方法之间都具有很高的一致性,无显著性差异,准确性高,因此ST损伤向量的罗盘显示方法是心肌缺血检测领域一种很有价值的诊断手段。     

基于全响应算法的故障电流相控开断研究

故障电流可控开断(CFI)的难点在于快速准确地检测到故障发生,估计出故障电流参数并预测出可用的过零点及判断出故障类型,为此提出了一种全响应算法。该算法基于电路的全响应原理,将含有指数分量的故障电流方程线性化,并使用最小二乘法求解;采用F0假设检验检测故障初始时刻,给出了故障相及故障类型的判据,确定了三相故障电流可控开断策略。利用MATLAB编程CFI控制策略处理程序,分别从PSCAD仿真的故障电流数据和录波数据进行了仿真和验证。结果表明:算法能在故障发生后3 ms内判断出故障类型,精度在±0.5 ms以内,能够满足三相故障电流可控开断的要求。     

g玻色子对轻核的影响

本文通过分别对 sd-IBM4中的 O(6)极限 U(36)U_6(sd)×U_6(ST)O_6(sd)×O_6(ST)O_3(d)×O_3(S)×O_3(T)O_3(J)×O_3(T),强耦合 SU(3)极限 U(36)U_6(sd)×U_6(ST)SU_3(sd)×SU_3(S)×SU_3(T)SU_3(sdS)×SU_3(T)O_3(J)×O_3(T)与 sdg-IBM4中的类SO(6)极限 U(90)U_(15)(sdg×U_6(ST)SU_(15)(sdg)×U_6(ST)U_5(sdg)×O_6(ST)SO_5(dg)×O_6(ST)O_3(dg)×O_3(S)×O_3(T)O_3(J)×O_3(T),强耦合 SU(3)极限 U(90)U_(15)(sdg)×U_6(ST)SU_(15)(sdg)×U_6(ST)SU_3(sdg)×SU_3(S)×SU_3(T)SU_3(sdgS)×SU_3(T)O_3(J)×O_3(T)的分析比较,表明 g 玻色子对 O(6)极限的典型能谱有较大的影响,当用类 O(6)极限同时拟合~(34)S 与~(34)Cl 的能谱时,对~(34)Cl 能得到很低的3_1~+态,而用 O(6)极限则不能,而且能谱结构也改变较大.但 g 玻色子对强耦合 SU(3)极限的低能态能谱与 E2跃进,则影响不大.     

基于db4小波变换和双ARM Cortex-M的电能质量分析系统

研究并开发了基于双ARM Cortex-M平台、利用小波变换检测暂态扰动电信号的电能质量分析系统原型.首先利用Mallat算法对电能信号进行离散小波分解,计算并比较高频区间的模极大值,进而确定扰动发生时间点并进行录波.在双ARM Cortex-M硬件平台上对该检测扰动的算法进行验证,通过电压中断和暂态振荡两种输入信号模型对系统进行测试,结果表明系统能在2ms内检测到电信号的突变,能准确实时地检测到暂态扰动出现的时刻.     

Using wavelet transforms for ECG characterization. An on-linedigital signal processing system

The rapid and objective measurement of timing intervals of the electrocardiogram (ECG) by automated systems is superior to the subjective assessment of ECG morphology. The timing interval measurements are usually made from the onset to the termination of any component of the EGG, after accurate detection of the QRS complex. This article describes a real-time system that uses wavelet transforms to overcome the limitations of other methods of detecting QRS and the onsets and offsets of P- and T-waves. Wavelet transformation is briefly discussed, and detection methods and hardware and software aspects of the system are presented, as well as experimental results     

一种面向心电信号处理的奇异谱分析改进算法

心电图(ECG)作为人体的关键生理信号被广泛应用于医疗领域,但在采集过程中心电信号容易受到噪声干扰而影响 信号质量。为此,设计了一种奇异谱分析(SSA)的改进算法用于心电信号降噪处理。奇异谱分析改建算法是在 SSA中的主 元重组(grouping)阶段引入逻辑回归(LR) 算法,将主元重组方式改进为自动重组,实现面向心电信号的 SSA 自监督降噪处 理。使用基于 AD620 的心电信号采集装置,构建53条心电信号测试集进行验证,使用奇异谱分析的改进算法,主元自动选择 的准确性为98.68%,重构的心电信号信噪比(SNR)由10.43 dB平均提高到20.17 dB,能够有效提取出清晰的PQRST 波,使 其在医疗领域心电信号检测与降噪方面具有很好的实用化前景。     

Design of efficient fractional operator for ECG signal detection in implantable cardiac pacemaker systems

A low power and high-performance digital electrocardiogram (ECG) detector has become a basic requirement in modern implantable cardiac pacemakers. A fractional operator-based digital ECG detector for modern pacemaker systems is proposed in this work. Instead of conventional thresholding, an adaptive slope prediction threshold is utilized for the detection of ECG peaks. A stochastic search-based algorithm, namely, cuckoo search algorithm, is used to design an optimal fractional operator that is used for ECG denoising. It has been found that the proposed adaptive slope prediction threshold increases the QRS complex detection performance. A low detection error rate (DER) ranges from 0.01% to 0.56%, positive predictivity (P+) ranges from 99.32% to 99.98%, sensitivity (Se) ranges from 99.45% to 99.98%, and a detection accuracy (Acc) ranges from 99.43% to 99.96% for different databases are achieved for the proposed ECG detector, which is better compared with the existing ECG detectors. The proposed design of fractional order operator based on the lattice wave digital filter (LWDF) requires a minimum number of the multipliers for its structural realization.     

Comparing stress ECG enhancement algorithms

There are two predominant types of noise that contaminate the electrocardiogram (EGG) acquired during a stress test: the baseline wander noise (BW) and electrode motion artifact, and electromyogram-induced noise (EMG). BW noise is at a lower frequency, caused by respiration and motion of the subject or the leads. The frequency components of BW noise are usually below 0.5 Hz, and extend into the frequency range of the ST segment during a stress test. EMG noise, on the other hand, is predominantly at higher frequencies, caused by increased muscle activity and by mechanical forces acting on the electrodes. The frequency spectrum of the EMG noise overlaps that of the ECG signal and extends even higher in the frequency domain. In this article, the authors review some of the published ECG enhancing techniques to overcome the noise problems, and compare their performance on stress ECG signals under adverse noise scenarios. They also describe the filter bank-based ECG enhancing algorithm     

基于区域极值法心电QRS波的检测

目前,心电QRS波检测的方法很多,其中小波变换较为流行。其核心策略通常是在某一尺度或某几个尺度内搜索小波变换模极大—极小值对之间的过零点,此点对应R波峰值位置。然而,此方法策略存在一定的局限性,它只对一部分病例具有较好的检测效果,对于其它病例则不能较好地检测到R波峰值点,存在不同程度的偏差。根据医学心电常识,R波在心电信号一定区域内幅值为最大,只要确定了搜索区域,搜索到极大值点即为R波峰值点。本文结合二次B-样条小波变换算法提出一种区域极值检测法。通过美国MIT/BIH心电标准数据库进行实验验证表明,该方法克服了此局限性,对108和207以外的所有病例都具有较高的检测率和较好的检测效果,同时经过ARM平台实际应用验证了本算法完全满足实时分析的需求。     

Robustness study of ECG biometric identification in heart rate variability conditions

Electrocardiogram (ECG) has been actively proposed as an aliveness biometric for years. In realistic application, this type of biometrics still needs to be verified in some conditions related to the practical use. Therefore, we propose in this paper the study of two themes that concern its realistic application. First, the single beat of an electrocardiogram (SB‐ECG) is used as the ECG feature, which aims to decrease the time to acquire the ECG from the individual. Second, for the robustness study of ECG features to heart rate variability (HRV), we investigate the relation between the correct identification and the HRV conditions. The HRV‐ECG data is set up and collected by using the Bio‐Pac system. The overall processes of the proposed method are the following: Continuous wavelet transform (CWT) is used to analyze each SB‐ECG. Then the root‐mean square (RMS) value of total energy of the wavelet coefficients of each P, QRS, and T segment is calculated. Next, the Fisher linear discriminant analysis (FLDA) is applied to all sets of the RMS value for dimension reduction. Lastly, the normalized Euclidean distance is implemented as the classifier. The experimental results demonstrate that, with the proposed method, the classification achieves 97% accuracy and the robustness study achieves over 80% accuracy with HRV‐ECG change up to 20%. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Time-frequency analysis of heart-rate variability

We present the results of a study that shows the viability of a new technique for the diagnosis and monitoring of myocardial ischemia that is based on the utilization of heart-rate variability (HRV) information. Ischemia is understood as being the lack of oxygen supply to the heart, a situation that in an extreme and irreversible case results in acute myocardial infarction (AMI), a reason for which early detection and treatment is of great interest. The treatment of ischemia can be approached via the evolution of the ECG, and especially from one of the parameters extracted from it-the ST segment (ECG signal between S and T waves) deviation. The utility of this measure is found in its capacity for detecting abnormalities in the conduction of the cardiac impulse that are associated with the presence of ischemia     

EWT算法在ECG信号滤波中的研究

针对经验模态分解(EMD)算法存在的模态混叠问题和集合经验模态分解(EEMD)算法实时性不足的缺点,采用EMD与小波分析相结合的EWT算法,对ECG信号的频谱自适应分割,在分割区间上构建小波滤波器组,提取具有紧支撑的单分量成分,剔除直流分量和噪声余项,并将其余分量重构。实验数据来自MIT-BIH数据库中真实的心电图(ECG)信号,仿真结果表明,该算法能有效去除ECG信号中的基线漂移和工频干扰,信噪比(SNR)、均方根误差(RMSE)和自相关系数(AC)优于其他两种自适应算法EMD和EEMD;算法整体运行时间小于1 s,满足了心电监测的实时性与准确性要求。