心电信号的aTrous小波变换综合检测方法

心电信号反映了心脏有节律的活动。R波、P波和T波是去、复极时产生的突变信号,是典型的峰值奇异信号。信号的突变点检测是小波变换应用的一个重要方面。确定QRS波群的具体形态和起止点,检测P波、T波特征点是心电图分析的难点。研究了信号的二进样条小波按aTrous（多孔）算法进行的变换,构建了系列检测方法,来检测和识别QRS波群、P波、T波的具体的形态和位置。实验结果表明,所提出的综合算法具有较好的适应性,能很好地抑制基线漂移,消除高频干扰,克服了大T波、大S波、高U波波形自身病态因素对综合检测产生的影响。     

基于数学形态滤波和提升小波变换的R波检测

利用数学形态学与提升小波变换相结合的方法对心电信号进行分析处理。先用数学形态方法对心电信号进行滤波预处理,可以有效地去除高频白噪声与低频的基线飘移,再利用提升小波变换对处理后的心电信号进行多分辨分析,得出各层逼近信号与细节信号,并在此基础上结合不应期、自适应阈值和回溯检漏等方法,提出了一种动态的R波检测算法,使得QRS波群的检出率达到99.89%。     

面向智能服装健康监护系统的心电信号基线漂移处理*

面向智能服装的健康监护系统心电信号存在严重的基线漂移,针对基漂去除的需要,提出了基于基线漂移阈值的分级处理方法。首先采用滑动窗口中值滤波算法对心电信号进行滤波,并计算出基线漂移的程度大小,当其大于给定的阈值时,采用小波变换得到QRS波群的位置信息和信号的特点来变动滑动窗口大小。中值滤波和小波算法可以在两个处理平台上并行运行,提高了运算速度;最后,运用该算法分别对模拟和实际的基线漂移进行处理,并与其他算法的处理结果进行了比较,结果表明该算法具有较好的实时性和处理效果。     

基于非线性能量算子的心电信号R波检测方法

将Marr小波变换和非线性能量算子相结合实现了心电信号的R波检测,心电信号的Marr小波分解信号很好地抑制了各种噪声干扰,结合非线性能量算子运算可突出了QRS波的特征点,使得阈值检测便于实施,利用修正策略提高了R波检测率,经MIT/BIH标准心律失常数据库验证,R波的检测率可达到99.7%,该方法对于心电信号的自动分析系统具有应用价值。     

Application of support vector machine for the detection of P- and T-waves in 12-lead electrocardiogram

Electrocardiogram (ECG) is characterized by a recurrent wave sequence of P, QRS and T-wave associated with each beat. The performance of the computer-aided ECG analysis systems depends heavily upon the accurate and reliable detection of these component waves. This paper presents an efficient method for the detection of P- and T-waves in 12-lead ECG using support vector machine (SVM). Digital filtering techniques are used to remove power line interference and base line wander. SVM is used as a classifier for the detection of P- and T-waves. The algorithm is validated using original simultaneously recorded 12-lead ECG recordings from the standard CSE ECG database. Significant detection rate of 95.43% is achieved for P-wave detection and 96.89% for T-wave detection. The method successfully detects all kind of morphologies of P- and T-waves. The on-sets and off-sets of the detected P- and T-waves are found to be within the tolerance limits given in CSE library.     

Identification of QRS complexes in 12-lead electrocardiogram

A method for the detection of QRS complexes in 12-lead electrocardiogram (ECG) using support vector machine (SVM) is presented in this paper. Digital filtering techniques are used to remove base line wander and power line interference. SVM is used for the identification of QRS complexes in the processed signal. The performance of the algorithm is evaluated against the standard CSE ECG database. The results indicated that the algorithm achieved 99.75% of the identification rate. The percentage of false positive and false negative is 1.61% and 0.26%, respectively. The performance of the proposed algorithm is found to be better than published results of the other QRS detectors tested on the same database. The proposed method functions reliably even under the conditions of poor signal quality in the ECG data.     

基于小波变换和希尔伯特包络分析的QRS波检测算法

提出一种基于双正交小波变换和Hilbert变换的QRS波检测算法。首先，通过双正交小波变换分解与重构，消除高频噪声，同时突出R峰位置，构造出有利于QRS波检测的检测层。然后，对信号求差分和希尔波特变换，进一步抑制P波、T波以及基线漂移等噪声。最后，在计算得到的包络信号上根据自适应阈值及决策规则进行R峰检测。根据MIT-BIH心率失常数据库有标注的临床数据进行验证，QRS波检测结果准确率达到99.01%，同时算法具有不错的鲁棒性和实时性。     

基于小波变换与形态学运算的R波检测算法

本文提出了一种基于小波变换与形态学运算的R 波检测算法。采用二进Marr小波的Mallat算法对心电信号作多分辨率分解，利用数学形态学运算突出信号的峰谷点特征，将小波变换模极大值检测原理与形态学峰谷检测算法相结合，不仅可以实现对 R 波的准确检测和精确定位，同时也具有较好的算法实时性。     

QRS detection based on wavelet coefficients

Electrocardiogram (ECG) signal processing and analysis provide crucial information about functional status of the heart. The QRS complex represents the most important component within the ECG signal. Its detection is the first step of all kinds of automatic feature extraction. QRS detector must be able to detect a large number of different QRS morphologies. This paper examines the use of wavelet detail coefficients for the accurate detection of different QRS morphologies in ECG. Our method is based on the power spectrum of QRS complexes in different energy levels since it differs from normal beats to abnormal ones. This property is used to discriminate between true beats (normal and abnormal) and false beats. Significant performance enhancement is observed when the proposed approach is tested with the MIT-BIH arrhythmia database (MITDB). The obtained results show a sensitivity of 99.64% and a positive predictivity of 99.82%.     

一种基于特征增强的自适应阈值R波检测算法

为提高心电信号R波检测准确度,提出了一种基于特征增强的R波检测算法。首先经过小波分析去除心电信号的高频噪声和基线漂移,然后对信号进行差分运算,选取各数据点前后连续10个差分的最大和最小值做乘积运算,达到增强R波特征的目的,最后设定两个自适应阈值,对全部数据完成检测。实验结果:经过MIT-BIH Arrhythmia Database数据验证,R波检测准确度Acc可达99.57%,敏感度Se高达99.76%,真阳性率+P高达99.82%。将得到的结果与已有文献中的方法进行比较,本文算法简单,实时性好,检测准确率高,更符合实际临床应用的需求。     

Motion artifact removal based on periodical property for ECG monitoring with wearable systems

Motion artifact removal (MR) is one of the essential issues in processing raw ECG signals since it could not be simply solved by using classic filtering. In this paper, a QRS detection based Motion Artifact Removal algorithm (QRSMR) is proposed. The proposed method detects the entire QRS complex and removes the noise between two QRS complexes, while recovering P and T-waves. As verified in the tests on simulated noisy ECG signals, QRSMR outputs with seriously contaminated ECG signals have an increase of the correlation with their original clean signals from 40% to nearly 80%, demonstrating the improved noise removal ability of QRSMR. Moreover, in the tests on real ECG signals measured on volunteers with a flexible wearable ECG monitoring device developed at Fudan University, QRSMR is able to recover P-wave and T-wave from the contaminated signal, which shows its enhanced performance on motion artifact reduction comparing with adaptive filtering method and other methods based only on empirical mode decomposition.     

一种利用小波变换逼近信号滤除心电图基线漂移的方法

该文提出了一种利用小波变换逼近信号滤除心电图(ECG)基线漂移的方法。该方法的基本思想是:通过对ECG信号进行多尺度分解,利用分解后所得的逼近信号充分逼近ECG中的基线漂移噪声的特性,从而滤除ECG中的基线漂移分量。将该方法应用于MIT-BIHDatabase眼1演提供的30分钟的真实ECG记录上,结果显示该方法能较好地滤除ECG基线漂移,且对信号的其他成分损害较小。     

基于形态小波的QRS波检测算法

根据心电信号中QRS波群的特点,提出了一种基于小波变换和数学形态学相结合的形态小波检测算法。小波变换方法对突变信号在时频域都具有优异的辨识能力及“可变焦距”的优良特性;数学形态学是基于信号局部特征的,能够在时域上提取信号的峰谷信息。将这两种方法结合起来,利用MIT/BIH心电数据库进行验证,QRS波群的检出率高达99.84%。     

基于第二代小波变换的心电信号分析算法

为了改进用于计算机辅助诊断的心电信号处理中QRS组波检测速度以及实现心电信号的精确重构，本文提出利用第二代小波变换即提升格式对心电信号进行处理的方法。采用双正交样条小波滤波器，与此同时，给出提升方案。为了验证方法的实效性，对美国MIT-BIT数据库中的几组心电信号进行了初步的处理与试验分析，结果表明该方法不仅改善了检测速度和重构的精确性，同时也为心电信号的压缩处理提供了方便。     

Wavelet transform analytics for RF-based UAV detection and identification system using machine learning

In this work, we performed a thorough comparative analysis on a radio frequency (RF) based drone detection and identification system (DDI) under wireless interference, such as WiFi and Bluetooth, by using machine learning algorithms, and a pre-trained convolutional neural network-based algorithm called SqueezeNet, as classifiers. In RF signal fingerprinting research, the transient and steady state of the signals can be used to extract a unique signature from an RF signal. By exploiting the RF control signals from unmanned aerial vehicles (UAVs) for DDI, we considered each state of the signals separately for feature extraction and compared the pros and cons for drone detection and identification. Using various categories of wavelet transforms (discrete wavelet transform, continuous wavelet transform, and wavelet scattering transform) for extracting features from the signals, we built different models using these features. We studied the performance of these models under different signal-to-noise ratio (SNR) levels. By using the wavelet scattering transform to extract signatures (scattergrams) from the steady state of the RF signals at 30 dB SNR, and using these scattergrams to train SqueezeNet, we achieved an accuracy of 98.9% at 10 dB SNR.     

小波滤波与QRS波检测

利用双正交样条小波等效滤波器,实现了ECG信号的小波分解和重建。分析心电信号奇异点与其小波变换模极大值对的零交叉点的关系,提出了心电信号QRS波检测的算法。在检测算法中还使用了一系列策略来提高算法的抗干扰能力和QRS检测的准确性。经MIT/BIH心律失常数据库验证,QRS波的正确检测率达99.506%。最后将该算法应用到Windows Mobile智能手机上的心电监护系统中,达到令人满意的效果。     

Detection and delineation of P and T waves in 12-lead electrocardiograms

Abstract: This paper presents an efficient method for the detection and delineation of P and T waves in 12-lead electrocardiograms (ECGs) using a support vector machine (SVM). Digital filtering techniques are used to remove power line interference and baseline wander. An SVM is used as a classifier for the detection and delineation of P and T waves. The performance of the algorithm is validated using original simultaneously recorded 12-lead ECG recordings from the standard CSE (Common Standards for Quantitative Electrocardiography) ECG multi-lead measurement library. A significant detection rate of 95.43% is achieved for P wave detection and 96.89% for T wave detection. Delineation performance of the algorithm is validated by calculating the mean and standard deviation of the differences between automatic and manual annotations by the referee cardiologists. The proposed method not only detects all kinds of morphologies of QRS complexes, P and T waves but also delineates them accurately. The onsets and offsets of the detected P and T waves are found to be within the tolerance limits given in the CSE library.     

心电信号去噪的数学形态学滤波器

为探索验证一种基于数学形态滤波器的去除心电基线漂移和工频干扰的高性能滤波器设计方法,借鉴数学形态学一维信号滤波原理,提出自适应阈值ECG去噪算法的思路,讨论了3σ统计准则在ECG自适应阈值滤波中的作用,利用改进的算法对心电图中常见的工频干扰和基线漂移进行校正。通过对MIT-BIH心率变异数据库中多组数据的仿真验证研究,验证了该算法能有效实现心电信号的噪声预处理;数学形态学理论在心电信号处理中具有良好性能,是实时处理一维生物医学信号有潜力的工具。     

基于SoC FPGA的心电信号检测系统设计

设计实现了一种基于片上系统现场可编程门阵列( SoC FPGA)的心电信号( ECG)检测系统.系统通过具有高输入阻抗、高共模抑制比和低噪声的前置采集放大电路,实现心电信号的拾取和预处理.通过基于SoC FPGA的硬件平台和移植的嵌入式Linux开发环境的软硬协同设计方式,完成了心电信号的A/D转换、VGA显示、Micro SD卡数据存储和心电信号算法处理,能够对心电信号进行小波分析和QRS波检测,实现了对心电信号的采集、显示、存储和处理.     

小波包分析和形态学滤波相结合的心电图QRS波群定位

心电图波形特征提取是针对-维心电信号的弱信号特征提取.如何排除各种干扰,提取出心电波形特征,并准确定位心电信号中P波、QRS波群、T波,一直是心脏病智能诊断的难点和热点问题,其中QRS波群的定位又是其它波定位的重要依据.利用形态学和小波包理论相结合的方法对这一问题进行了探讨,提出了QRS波群定位和滤除基线漂移的方法.实验证明提出的方法速度较快,能较准确的定位QRS波群、有效的去除基线漂移.