心电信号特征量的小波提取算法

目的:为了更准确地利用心电图(ECG)进行临床生理疾病诊断,提高心电信号的自动分析准确度.介绍了一种利用小波变换的时频局部化特性以及多分辨率特性对心电信号进行处理的算法.方法:使用定位准确.计算简便的二阶微分Mart小波使用多孔算法来对ECG中QRS波群进行标定.结果:将算法应用到MIT/BIH国际标准心电数据库进行仿真.结论:通过仿真证明,该算法能够很精确地定位QRS波群,为心电信号的后续研究打好基础.     

基于独立元分析和联合小波熵检测多导联ECG信号的QRS

QRS波群的准确定位是ECG信号自动分析的基础。为提高QRS检测率,提出一种基于独立元分析（ICA）和联合小波熵（CWS）检测多导联ECG信号QRS的算法。ICA算法从滤波后的多导联ECG信号中分离出对应心室活动的独立元;然后对各独立元进行连续小波变换（CWT）,重构小波系数的相空间,结合相空间中的QRS信息对独立元排序;最后检测排序后独立元的CWS得到QRS信息。实验对St.Petersburg12导联心率失常数据库及64导联犬心外膜数据库测试,比较本文算法与单导联QRS检测算法和双导联QRS检测算法的性能。结果表明,该文算法的性能最好,检测准确率分别为99.98%和100%。     

一种QRS波群实时检测方法

结合模板匹配和改进的导数阈值法,提出了一种QRS波群实时检测方法CT2(combination method of template matching and improved derivative threshold)。首先,预采集一段ECG信号,使用高斯函数构造QRS模板;然后将实时采集的ECG信号使用CT2检测R波位置。为了比较算法检测精度和效率,使用CT2和基于小波模极大值的方法进行了对比。结果表明,CT2检测精度与基于小波模极大值的方法相当,但运算时间大大缩短,适于实时检测。     

一种心电波群实时检测的新算法

本文提出了一种新的ＱＲＳ波群检测方法－斜率匹配最大值检测法，该方法与现有的其他ＱＲＳ波群检测方法相比，具有算法简单，抗噪声性能好的特点。     

基于小波变换的QRS综合波的检测

本文描述了基于二进制小波变换（DyWT),ECG信号中QRS综合波的检测。设计－小波它适合于QRS检测,将基于心电信号的特殊的特征的特征为小波的尺度。DyWT较之其它方法最基本的优点为强有力的抑制噪声检测以及在分析随时间变化ECG波形时的灵活性。     

QRS波群自动分类技术研究进展

本文综述了QRS波群自动分类技术的关键技术及其研究方向,内容涉及QRS波群的描述方法、分类方法和训练数据集这三个重要影响因素。     

犬心肌缺血再灌注损伤心电图Ⅱ导联QRS波群的演变

目的观察杂种犬心肌缺血再灌注损伤过程心电图(ECG)的动态变化规律,为研究干细胞移植对犬心肌缺血再灌注损伤后的治疗作用提供基础。方法选用杂种犬24只,结扎冠状动脉左前降支中远1/3处,分3组分别于30min、60min、90min后松开。应用MPA-2000生物信号分析系统,在结扎前后及松开后连续动态记录ECGII导联QRS波群的变化。结果在开始结扎阻断冠状动脉血流时,QRS主波向上Rs型;重新开放血流血管再通之初,83.3%(20/24)的犬QRS波表现为主波向下rS型或QS型,伴QRS波增宽,然后r波波幅逐渐增大,逐渐演变成主波向上的Rs型,QRS时程恢复正常。15min、30min和大于30min的演变率分别为25.00%(6/24)、41.7%(10/24)和16.7%(4/24);且演变率与冠脉阻断时间相关。结论犬心肌缺血再灌注后ECG变化有一定规律,QRS波群变化趋势有可能作为心肌细胞功能恢复程度以及心肌保护效果较为直观的判断指标。     

心电信号的计算机分析—特征检测

心电信号的特征检测是心电计算机分析的核心内容,本文报导了作者采用的方法,包括Ｒ波定位,ＱＲＳ波群区分点检测,Ｐ波,Ｔ波区发点的检测,Ｕ波的检测以及Ｐ－Ｑ,Ｓ－Ｔ段的分析,测试结果表明这些方法的优越性和可靠性,在ＳＴ段分析上有较大突破。     

几种脊椎动物宽频带心电图波形与心电向量环特点的比较

对小鼠、家鸽、蟾蜍、鲫鱼4种脊椎动物宽频带心电图Ⅱ导联的波形特点及QRS额面心电向量环的位置、形态特点进行了比较研究。结果发现,(1)宽频带心电图Ⅱ导联QRS波群小鼠、蟾蜍、鲫鱼QRS波群的主波均向上,而家鸽的主波向下;(2)QRS波群时程(ms)小鼠88±09,家鸽365±14,蟾蜍790±110,鲫鱼283±57;(3)QRS心电向量环的位置家鸽的位于-90°～-180°象限内,这是家鸽心电图Ⅱ导联QRS波群主波向下的根本原因;小鼠、蟾蜍、鲫鱼的均位于0°～90°象限内,与它们的QRS波群主波向上相一致;(4)QRS心电向量环的形状;小鼠的QRS向量环较其它3种动物的要大,蟾蜍的最小。鲫鱼的不规则,有的呈三角形,有的呈“8”字形,还有的呈半圆形,这是导致鲫鱼的QRS波群出现较多切迹和扭挫的原因。     

基于QRS波群的心律失常辅助诊断模型研究

基于对QRS波群的特征变量提取。利用减法聚类和自适应模糊神经网络构建心律失常辅助诊断模型,分析不同训练数据集对模型测试结果的影响。实验结果表明。该模型能准确识别不同类型的QRS波群,使用不同训练数据集对诊断结果存在影响,为进一步实现更复杂的心律失常辅助诊断模型提供方法。     

Revisiting QRS Detection Methodologies for Portable,Wearable, Battery-Operated,and Wireless ECG Systems

Cardiovascular diseases are the number one cause of death worldwide. Currently, portable battery-operated systems such as mobile phones with wireless ECG sensors have the potential to be used in continuous cardiac function assessment that can be easily integrated into daily life. These portable point-of-care diagnostic systems can therefore help unveil and treat cardiovascular diseases. The basis for ECG analysis is a robust detection of the prominent QRS complex, as well as other ECG signal characteristics. However, it is not clear from the literature which ECG analysis algorithms are suited for an implementation on a mobile device. We investigate current QRS detection algorithms based on three assessment criteria: 1) robustness to noise, 2) parameter choice, and 3) numerical efficiency, in order to target a universal fast-robust detector. Furthermore, existing QRS detection algorithms may provide an acceptable solution only on small segments of ECG signals, within a certain amplitude range, or amid particular types of arrhythmia and/or noise. These issues are discussed in the context of a comparison with the most conventional algorithms, followed by future recommendations for developing reliable QRS detection schemes suitable for implementation on battery-operated mobile devices.     

Predicting the QRS complex and detecting small changes using principal component analysis.

In this paper, a new method for QRS complex analysis and estimation based on principal component analysis (PCA) and polynomial fitting techniques is presented. Multi-channel ECG signals were recorded and QRS complexes were obtained from every channel and aligned perfectly in matrices. For every channel, the covariance matrix was calculated from the QRS complex data matrix of many heartbeats. Then the corresponding eigenvectors and eigenvalues were calculated and reconstruction parameter vectors were computed by expansion of every beat in terms of the principal eigenvectors. These parameter vectors show short-term fluctuations that have to be discriminated from abrupt changes or long-term trends that might indicate diseases. For this purpose, first-order poly-fit methods were applied to the elements of the reconstruction parameter vectors. In healthy volunteers, subsequent QRS complexes were estimated by calculating the corresponding reconstruction parameter vectors derived from these functions. The similarity, absolute error and RMS error between the original and predicted QRS complexes were measured. Based on this work, thresholds can be defined for changes in the parameter vectors that indicate diseases.     

SVM-based algorithm for recognition of QRS complexes in electrocardiogram

Among all electrocardiogram (ECG) components, the QRS complex is the most significant feature. This paper presents a new algorithm for recognition of QRS complexes in the electrocardiogram (ECG) based on support vector machine (SVM). Digital filtering techniques are used to remove power line interference and baseline wander in the ECG signal. SVM is used as a classifier to delineate QRS and non-QRS regions. Algorithm performance was evaluated against the standard CSE ECG database. The results indicated that the algorithm achieved 99.3% of the detection rate. The percentage of false positive and false negative was 12.4 and 0.7% respectively. It could function reliably even under the condition of poor signal quality of the ECG signal.     

Discrete-wavelet-transform-based noise removal and feature extraction for ECG signals

Nowadays, doctors use electrocardiogram (ECG) to diagnose heart diseases commonly. However, some nonideal effects are often distributed in ECG. Discrete wavelet transform (DWT) is efficient for nonstationary signal analysis. In this paper, the Symlets sym5 is chosen as the wavelet function to decompose recorded ECG signals for noise removal. Soft-thresholding method is then applied for feature detection. To detect ECG features, R peak of each heart beat is first detected, and the onset and offset of the QRS complex are then detected. Finally, the signal is reconstructed to remove high frequency interferences and applied with adaptive searching window and threshold to detect P and T waves. We use the MIT-BIH arrhythmia database for algorithm verification. For noise reduction, the SNR improvement is achieved at least 10 dB at SNR 5 dB, and most of the improvement SNR are better than other methods at least 1 dB at different SNR. When applying to the real portable ECG device, all R peaks can be detected when patients walk, run, or move at the speed below 9 km/h. The performance of delineation on database shows in our algorithm can achieve high sensitivity in detecting ECG features. The QRS detector attains a sensitivity over 99.94%, while detectors of P and T waves achieve 99.75% and 99.7%, respectively.     

Quantitative analysis of variability of electrocardiograms typical for polymorphic arrhythmias

A new approach to the analysis of variability of electrocardiograms (ECGs) typical of polymorphic arrhythmias is developed. In these ECGs, separate QRS complexes can be often hardly identified. As a result, the mathematical methods that have been elaborated hitherto are not suitable for such arrhythmias. The approach presented here is based on the quantitative estimation of the variability of neighboring parts of the ECG. In this case, the necessity of the identification of separate QRS complexes ceases to be significant. Based on this approach, the analysis of normalized ECG variability is developed in the framework of which two indices that characterize the oscillation variability and its changes in time are related to a part of the ECG and/or the ECG as a whole. Variations of these indices allow both the polymorphism of a separate ECG to be estimated and different ECGs to be compared with each other. The method presented may be useful in studies of the mechanisms and in the diagnosis of polymorphic arrhythmias.     

Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains

This paper presents a new ECG denoising approach based on noise reduction algorithms in empirical mode decomposition (EMD) and discrete wavelet transform (DWT) domains. Unlike the conventional EMD based ECG denoising approaches that neglect a number of initial intrinsic mode functions (IMFs) containing the QRS complex as well as noise, we propose to perform windowing in the EMD domain in order to reduce the noise from the initial IMFs instead of discarding them completely thus preserving the QRS complex and yielding a relatively cleaner ECG signal. The signal thus obtained is transformed in the DWT domain, where an adaptive soft thresholding based noise reduction algorithm is employed considering the advantageous properties of the DWT compared to that of the EMD in preserving the energy in the presence of noise and in reconstructing the original ECG signal with a better time resolution. Extensive simulations are carried out using the MIT-BIH arrythmia database and the performance of the proposed method is evaluated in terms of several standard metrics. The simulation results show that the proposed method is able to reduce noise from the noisy ECG signals more accurately and consistently in comparison to some of the stateof-the-art methods.     

Effect of adaptive motion-artifact reduction on QRS detection

Motion artifact resulting from electrode and patient movement is a significant source of noise in ECG, EEG, EMG, and impedance pneumography recording. Noise resulting from motion is particularly troublesome in ambulatory ECG recordings, such as those made during Holter monitoring or stress tests, because the bandwidth of the motion artifact overlaps with the ECG signal bandwidth. The authors investigated the effect of an adaptive motion-artifact removal algorithm on the performance of a standard QRS detector. They made four ECG recordings on each of the three subjects while manually generating artifact. Adaptive noise removal was applied to the ECG signal using a skin-stretch signal as the noise reference. Adaptive noise removal reduced the number of false QRS detections in the records from 380 to 104, for an average reduction in false detections of 72.6%. False-detection reductions for individual records ranged from 12% to 93%.     

A lead dependent ischemic episodes detection strategy using Hermite functions

In this work a new strategy for automatic detection of ischemic episodes is proposed. A new measure for ST deviation based on the time–frequency analysis of the ECG and the use of a reduced set of Hermite basis functions for T wave and QRS complex morphology characterization, are the key points of the proposed methodology.Usually, ischemia manifests itself in the ECG signal by ST segment deviation or by QRS complex and T wave changes in morphology. These effects might occur simultaneously. Time–frequency methods are especially adequate for the detection of small transient characteristics hidden in the ECG, such as ST segment alterations. A Wigner–Ville transform-based approach is proposed to estimate the ST shift. To characterize the alterations in the T wave and the QRS morphologies, each cardiac beat is described by expansions in Hermite functions. These demonstrated to be suitable to capture the most relevant morphologic characteristics of the signal. A lead dependent neural network classifier considers, as inputs, the ST segment deviation and the Hermite expansion coefficients. The ability of the proposed method in ischemia episodes detection is evaluated using the European Society of Cardiology ST–T database. A sensitivity of 96.7% and a positive predictivity of 96.2% reveal the capacity of the proposed strategy to perform ischemic episodes identification.