一种适用于心率变异分析的R波检测与定位算法

在心率变异分析中,不仅要可靠地检测出 R 波,而且要对 R 波的峰值点进行准确定位。本文提出一种简单有效的 R 波检测方法,它不仅能够有效地抑制心率变异分析中的基线漂移、工频噪声、肌电、运动伪迹等干扰,提高实时检测 R 波准确率,并且能够对 R 波的峰值点准确定位。由于算法简单,适用于便携式心率变异分析仪。     

基于改进DTW的心电图ST段识别

在心电图诊断中,经常用到ST段的偏移和形态变化分析,由于ST段难以精确定位,使得ST段形态分析难以直接进行。本文考虑到ST段起点J难以确定,但S点与ST段融合后有较好的区分度,所以将S点到T波起点这段作为区分ST形态的数据段。首先利用小波变换准确地确定S点和ST段终点(T波起点),提取出数据段平滑后,经改进动态时间规整(dynamic time warping,DTW)算法即可得到ST段的形态。经MIT/BIHST-T数据库的验证,该方法能较好地识别出心电图ST段的5种常见压低形态,即上斜型、下陷型、水平型、下垂型、弓背型。     

基于几何算法的T波终点检测研究

目的:用一种精确有效的方法实现T波终点检测。方法:对心电信号进行小波变换,定位R波后,通过准确地定位T波的检测区间,避免心率和P波的影响,在确定的区间上根据模极值检测出所有T波的顶点,用一种简单实用的几何方法检测出T波的终点。将本文的算法用于QT数据库中,评价实验结果。结果:本文的算法标注能力为(-0.35225±18.5869)ms,达到甚至超过了QT数据库里专家手工标注的水平。结论:文中采用T波终点检测的方法在存在噪声和基线漂移以及T波形态多变的情况下鲁棒性都比较好,并且计算简单,易于实现。     

一种适用于Holter系统的ST段分析方案

提出一种ST段分析方案用于辅助医生的快速读图和诊断。采用中值滤波方法拟合和提取基线 ,进而消除基线漂移。利用顺序统计滤波器去除高频噪声 ,采用小波分析方法进行QRS主波的定位和检测 ,交互式方法定位ST段起点和终点 ,估计所截取的ST段与基线值之间的平均偏差 ,做出ST段偏移量的整体趋势图。回放系统可以使医生根据ST段整体趋势图 ,任意选取想要看的原始ECG信号 ,达到辅助医生快速读图和诊断的目的。     

基于遗传算法和神经元网络的心电信号T波检测

为了提高T波检测准确率,解决由于反向传播神经元网络(BP神经元网络)连接权值和阈值的初始值选择不合适而导致的无解问题,本研究结合求全局最优解近似值的遗传算法(GA)和求局部最优解精确值的传统BP神经元网络所使用的梯度法,用于T波检测.首先,用GA求得BP神经元网络权值和阈值的全局最优解的近似值;然后,把该近似值作为初始值,训练该神经元网络;最后,用训练好的BP神经元网络识别T波.考虑到T波波峰一定是小波变换(WT)模极大值对,但是反过来,WT模极大值对不一定是T波波峰,首先检测到R波之后一定范围内的WT模极大值对,称为准T波(备选T波),再用训练好的BP神经元网络对刚才检测出的准T波进行筛选和识别,最后确认出真正的T波.MIT数据库实验表明用这样的组合方法检测T波,其检测准确率可达到98%.采取小波变换、GA、BP神经元网络的组合可提高识别T波的正确率.     

新的P波检测系统的研究食道P波的前置放大和硬件提取

一、提取P波的必要性及其难度:心房波的有无及其形态,以及心房波与心室波间距(即P—R间距)的大小及其变化,也就是说P波在心电图中的有无及其相对位置对心律不齐的诊断极为重要.但体表心电图之P波由于测试点不易靠近心房,故其幅度远小于R波,而在心律不齐病例中P波相对位置不固定,而且常被淹没在R波或T波之中,造成诊断上的困难.因食道靠近心房,故食道P波幅度远大于体表P波,因而有利于心电图的临床诊断.但是单极食道心电图的QRS波幅度往往远大于P波,而且由于受食道蠕动等可能因素的影响,造成单极食道心电图基线漂移很大,且波形复杂,仍不能满足临床诊断的需要,特别是不适于计算机自动识别、测量,因而难于     

基于神经网络的ECG ST段识别个人计算机系统

我们提出一个基于神经网络的用于ECG ST段识别的个人计算机系统。此系统包括一个预处理器、神经网络和一个识别器。自适应共振理论(ART)用于实现系统中的神经网络对输入ECG的响应自组织的形成。网络中神经元间竞争、协调的相互作用使系统对噪声具有鲁棒性。预处理器检测R点并将ECG信号划分为心动周期。每个心动周期都输入神经网络。然后神经网络标记下J点和T波起点Ton的大致位置。识别器根据所标记的位置确定     

基于CNN和频率切片小波变换的T波形态分类

心电实时监控是心血管疾病防治的重要手段.心电图中T波的变化是心肌缺血和心脏猝死等疾病的重要表征,T波形态自动识别是心电远程监控中一个重要问题.由于实时监护用心电的强噪声背景影响,传统的T波特征提取与分类算法遭遇瓶颈.提出一种结合切片频率小波变换和卷积神经网络的T波形态识别算法,包括:自动定位R波波峰位置与T波终点位置,...     

基于小波包变换的心电图ST段提取

目的:ST段是心电图的重要组成部分,其起始于心电图QRS波群的结束点并结束于T波的开始点。ST段的压低、分上斜形和下垂形压低、水平压低和以及鱼钩样改变都可以反映各种心脏疾病,所以,精确提取心电图ST段具有重要意义。因此,本文提出了基于小波包变换的心电图ST段精确提取算法。方法:首先对心电图信号进行去噪处理,滤去工频干扰信号以及基线漂移信号;然后,提取单周期的心电图信号;最后,引入小波包变换算法提取QRS波群、T波的主频带,重构QRS波群、T波的波形并确定ST段的始末位置。结果:本文算法在时域心电图上实现了ST的精确定位,提取了心电图的ST段。通过在经典数据库中的验证,本文算法具有非常好的表现。结论:实验结果表明,相较传统的对心电图加时间窗的方式提取ST段,本文算法可以精确提取心电图ST段,这为心电图ST段的自动精确识别,以及用于自动医疗检测与便携式医疗设备提供了依据。     

基于小波变换的ST-T段自动检测

本文提出了一个利用小波变换分析ST-T段的方法,该方法首先利用小波变换检测出心电信号的QRS波群,然后再同样利用小波的方法检测出等电位点、S点、J点、T波及其起点等特征点,利用这些特征点分析出反映ST-T段特征的参数,从而达到对ST-T段的检测分析.通过对MIT-BIH心电数据库部分数据的检测结果可以看出,相对于传统的检测方法(基于经验的),该方法可以大大减少噪声的干扰,提高ST-T段分析的准确度.     

Personal computer system for ECG ST-segment recognition based on neural networks

A personal computer system for electrocardiogram (ECG) ST-segment recognition is developed based on neural networks. The system consists of a preprocessor, neural networks and a recogniser. The adaptive resonance theory (ART) is employed to implement the neural networks in the system, which self-organise in response to the input ECG. Competitive and co-operative interaction among neurons in the neural networks makes the system robust to noise. The preprocessor detects the R points and divides the ECG into cardiac cycles. Each cardiac cycle is fed into the neural networks. The neural networks then address the approximate locations of the J point and the onset of the T-wave (T_on). The recogniser determines the respective ranges in which the J and T_on points lie, based on the locations addressed. Within those ranges, the recogniser finds the exact locations of the J and T_on points either by a change in the sign of the slope of the ECG, a zero slope or a significant change in the slope. The ST-segment is thus recognised as the portion of the ECG between the J and T_on points. Finally, the appropriateness of the length of the ST-segment is evaluated by an evaluation rule. As the process goes on, the neural networks self-organise and learn the characteristics of the ECG patterns which vary with each patient. The experiment indicates that the system recognises ST-segments with an average of 95·7 per cent accuracy within a 15 ms error and with an average of 90·8 per cent accuracy within a 10 ms error, and that characteristics of the ECG patterns are stored in the long term memory of the neural networks.     

冠心病患者心电图ST段改变与多排螺旋CT冠状动脉成像的关系分析

目的:通过对心电图ST段改变及多排螺旋CT冠状动脉成像（MSCTCA）检查在冠心病诊断中的比较分析，探讨冠心病诊断中心电图和MSCTCA检查的方法和作用，为临床诊断提供更多有效的信息。方法:选取临床诊断疑似或确诊为冠心病患者64例，进行心电图和MSCTCA检查，比较两种方法的一致性，不同部位、不同程度及不同类型病变检查的阳性率。结果:与MSCTCA方法相比，心电图ST段改变检查的灵敏度为56.82%，特异度为50%，总体符合率为54.69%，Kappa值为0.061，两种方法检查一致性较差，差异具有显著性（P<0.05）；I/avL/V1-5导联ST段改变阳性患者与Ⅱ/Ⅲ/avF导联ST段改变阳性患者相比，以MSCTCA检查的阳性率更高（P<0.05）；冠状动脉重度狭窄患者与轻度患者相比，心电图ST段改变阳性率显著升高（P<0.05）；中度狭窄患者与轻度患者相比，心电图ST段改变阳性率差异不显著（P>0.05）；多支冠状动脉病变患者心电图ST段改变阳性率显著高于单支管状动脉病变患者（P<0.05）。结论:心电图与MSCTCA检查一致性较差，临床上不宜单独使用单一检查方法；在重度冠状动脉狭窄患者及多支冠状动脉病变患者中，心电图ST段改变检出率较高，结果具有参考价值；I/avL/V1-5导联ST段改变阳性患者相比Ⅱ/Ⅲ/avF导联ST段改变阳性患者，MSCTCA检查阳性率更高，更具有检测意义。     

Automated detection of transient ST-segment episodes in 24h electrocardiograms

A novel automated system is presented for improved detection of transient ischaemic and heart rate-related ST-segment episodes in ‘real-world’ 24 h ambulatory ECG data. Using a combination of traditional time-domain and Karhunen-Loève transform-based approaches, the detector derives QRS complex and ST-segment morphology feature vectors and, by mimicking human examination of feature-vector time series and their trends, tracks the time-varying ST-segment reference level owing to clinically unimportant, non-ischaemic causes, such as slow drifts, axis shifts and conduction changes. The detector estimates the slowly varying ST-segment level trend, identifies step changes in the time series and subtracts the ST-segment reference level thus obtained from the ST-segment level to obtain the ST-segment deviation time series, which are suitable for detection of ST-segment episodes. The detector was developed using the Long-term ST database containing 24h ambulatory ECG records with human-expert annotated transient ischaemic and heart rate-related ST-segment episodes. The average ST episode detection sensitivity/positive predictivity obtained when using the annotations of the annotation protocol B of the database were 78.9%/80.7%. Evaluation of the detector using the European Society of Cardiology ST-T database as a test database showed average ST episode detection sensitivity/positive predictivity of 81.3%/89.2%, which are better performances, comparable with those of the systems being developed using the European database.     

模糊推理在基于心电图ST-T的心肌缺血判别中的应用

心电图(ECG)作为检测心肌缺血的一种方便、经济、无创的工具,其临床表现主要为ST-T段改变。由于心肌缺血很多情况下是瞬时发生,并且是无症状的,此时医务人员往往无法预料而不能及时采取干预措施,若能实现计算机的自动实时监控和及时预警,在心脏疾病诊断中具有重要的意义。本文融合目前比较公认的定量判断指标,主要包括:ST段偏移量、T波峰点幅值、ST/HR值,且在医务人员的协助指导下,应用模糊推理对心肌缺血进行了判别,经MIT-BIH数据库和公共数据库(LTST)中心电数据的验证,其敏感性和阳性预测值分别达到75%和78%,特异性和阴性预测值分别达到85%和87%。该方法更接近人的思维和认识,易于临床检测和工程实现。     

Diurnal Variations of ECG Parameters During 23-Hour Monitoring in Cardiac Patients With Ventricular Arrhythmias or Ischemic Episodes

ECG and physical activity (recorded with motion detectors) were continuously monitored during 23 hours in 31 male cardiac patients (81% with myocardial infarction). According to the occurrence of ventricular arrhythmias (VA) or ischemic episodes (IE), each patient was grouped in one of three diagnostic categories: neither VA nor IE, VA with or without IE, and IE only. Analysis of the ECG parameters was done beat-by-beat and averaged on a 1-min basis. Results were derived from the 2-hour means between 2 p.m. and 12 p.m. MANOVA revealed significant group differences for heart rate variability (greater for the group with VA), R-wave amplitude (higher for the group with IE), and P-wave amplitude (higher for the group with VA). Significant time effects were observed for all variables except QRS- and P-wave durations. As may be expected, physical activity and heart rate were lower at night. Heart rate variability, PQ-interval, PR-segment, QT-interval, ST-segment, and T-wave duration increased during the night. R-wave amplitude also increased but the relative P- and T-wave amplitudes decreased. The corrected QT-interval, QTc, was shorter at night and the ST-segment, J + 60-point, S-wave, and J-point amplitudes were less negative. Group X Time interactions were observed for T-wave amplitude. For this amplitude, the decrease during the night was prominent only for the VA group. The results of this study suggest that the three diagnostic groups can be differentiated by diverse ECG parameters.     

基于心电图的心肌缺血非线性分析

心电图是心肌缺血检测的一种方便、经济、无创的工具,其主要的临床表现为ST-T段变化.鉴于心脏是一个混沌系统,本文从非线性角度出发,对心电图特征参数序列进行分形维数和最大Lyapunov指数分析,其中特征参数序列包括：心电图RR间期,瞬时心率HR（heart rate）,ST段的平均幅值STmean,ST段最小幅值STmin,STmean与HR的比值STr,T波峰末间期TpTe,R波峰值Ramp,T波峰值Tamp.利用公共数据库Long-term ST database（LTST）中心肌缺血和非心肌缺血心电数据,通过统计t检验分析,除最大分形维数分析中的TpTe与最大Lyapunov指数分析中的Ramp和Tamp特征参数外,其余参数均有显著性差异.基于心电图的非线性分析为心肌缺血检测与诊断提供另一重要依据.     

Online Digital Filter and QRS Detector Applicable in Low Resource ECG Monitoring Systems

The present work describes fast computation methods for real-time digital filtration and QRS detection, both applicable in autonomous personal ECG systems for long-term monitoring. Since such devices work under considerable artifacts of intensive body and electrode movements, the input filtering should provide high-quality ECG signals supporting the accurate ECG interpretation. In this respect, we propose a combined high-pass and power-line interference rejection filter, introducing the simple principle of averaging of samples with a predefined distance between them. In our implementation (sampling frequency of 250 Hz), we applied averaging over 17 samples distanced by 10 samples (Filter10x17), thus realizing a comb filter with a zero at 50 Hz and high-pass cut-off at 1.1 Hz. Filter10x17 affords very fast filtering procedure at the price of minimal computing resources. Another benefit concerns the small ECG distortions introduced by the filter, providing its powerful application in the preprocessing module of diagnostic systems analyzing the ECG morphology. Filter10x17 does not attenuate the QRS amplitude, or introduce significant ST-segment elevation/depression. The filter output produces a constant error, leading to uniform shifting of the entire P-QRS-T segment toward about 5% of the R-peak amplitude. Tests with standardized ECG signals proved that Filter10x17 is capable to remove very strong baseline wanderings, and to fully suppress 50 Hz interferences. By changing the number of the averaged samples and the distance between them, a filter design with different cut-off and zero frequency could be easily achieved. The real-time QRS detector is designed with simplified computations over single channel, low-resolution ECGs. It relies on simple evaluations of amplitudes and slopes, including history of their mean values estimated over the preceding beats, smart adjustable thresholds, as well as linear logical rules for identification of the R-peaks in real-time. The performance of the QRS detector was tested with internationally recognized ECG databases (AHA, MIT-BIH, European ST-T database), showing mean sensitivity of 99.65% and positive predictive value of 99.57%. The performance of the presented QRS detector can be highly rated, comparable and even better than other published real-time QRS detectors. Examples representing some typical unfavorable conditions in real ECGs, illustrate the common operation of Filter10x17 and the QRS detector.     

Separation of electrocardiographic and encephalographic components based on signal averaging and wavelet shrinkage techniques

During ambulatory monitoring, it is often required to record the electroencephalogram (EEG) and the electrocardiogram (ECG) simultaneously. It would be ideal if both EEG and ECG can be obtained with one measurement. We introduce an algorithm combining the wavelet shrinkage and signal averaging techniques to extract the EEG and ECG components from an EEG lead signal to a noncephalic reference (NCR). The evaluation using simulation data and measured data showed that the normalized power spectrum unvaried in all frequency bands for the EEG components, and the sensitivity and specificity of R-wave detection for the ECG component were nearly 100%.     

Adaptive vector quantisation for electrocardiogram signal compression using overlapped and linearly shifted codevectors

A discrete semi-periodic signal can be described as x(n)=x(n+T+ΔT) +Δx,∀n, where T is the fundamental period, ΔT represents a random period variation, and Δx is an amplitude variation. Discrete ECG signals are treated as semi-periodic, where T and Δx are associated with the heart beat rate and the baseline drift, respectively. These two factors cause coding inefficiency for ECG signal compression using vector quantisation (VQ). First, the periodic characteristic of ECG signals creates data redundancy among codevectors in a traditional two-dimensional codebook. Secondly, the fixed codevectors in traditional VQ result in low adaptability to signal variations. To solve these two problems simultaneously, an adaptive VQ (AVQ) scheme is proposed, based on a one-dimensional (1D) codebook structure, where codevectors are overlapped and linearly shifted. To further enhance the coding performance, the Δx term is extracted and encoded separately, before 1D-AVQ is applied. The data in the first 3 min of all 48 ECG records from the MIT/BIH arrhythmic database are used as the test signals, and no codebook training is carried out in advance. The compressed data rate is 265.2±92.3 bits s⁻¹ at 10.0±4.1% PRD. No codebook storage or transmission is required. Only a very small codebook storage space is needed temporarily during the coding process. In addition, the linearly shifted nature of codevectors makes this easier to be hardware implemented than any existing AVQ method.     

Magnetocardiographic and Electrocardiographic Exercise Mapping in Healthy Subjects

In 12-lead electrocardiography (ECG), detection of myocardial ischemia is based on ST-segment changes in exercise testing. Magnetocardiography (MCG) is a complementary method to the ECG for a noninvasive study of the electric activity of the heart. In the MCG, ST-segment changes due to stress have also been found in healthy subjects. To further study the normal response to exercise, we performed MCG mappings in 12 healthy volunteers during supine bicycle ergometry. We also recorded body surface potential mappings (BSPM) with 123 channels using the same protocol. In this paper we compare, for the first time, multichannel MCG recorded in bicycle exercise testing with BSPM over the whole thorax in middle-aged healthy subjects. We quantified changes induced by the exercise in the MCG and BSPM with parameters based on signal amplitude, and correlation between signal distributions at rest and after exercise. At the ST-segment and T-wave apex, the exercise induced a magnetic field component outward the precordium and the minimum value of the MCG signal over the mapped area was found to be amplified. The response to exercise was smaller in the BSPM than in the MCG. A negative component in the MCG signal at the repolarization period of the cardiac cycle should be considered as a normal response to exercise. Therefore, maximum ST-segment depression over the mapped area in the MCG may not be an eligible parameter when evaluating the presence of ischemia. © 2001 Biomedical Engineering Society. PAC01: 8719Nn, 8719Hh, 8780Tq