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。     

mexican-hat小波在QRS波检测中的应用研究

提出了一种基于小波变换,结合多种判别修正方法的QRS波检测算法。首先采用mexican-hat小波对信号进行小波变换,在第3尺度上采用模极大值阈值法对R波进行检测。其次采用平面几何的数学方法定位Q波和S波。对于高尖P波和大T波造成的误检,采用弧度法进行纠正。对于高频干扰造成的影响用QRS时长法予以排除。该算法经过MIT-BIH Arrythmia Database的心电数据验证,取得了满意的结果。     

一种改进的心电图QRS波群检测算法

在利用小波变换检测QRS波群时,最关键的部分就是模极值配对,提出一种区域极值配对算法来检测R波。首先利用二次样条小波基函数和多孔(ATrous)算法对心电(ECG)信号进行小波变换求取模极值,用正极大值来确定搜索区域,以这个正极大值为起点,以这个确定区域为搜索范围,向左搜索负极大值点,将这两个极值配对,他们之间的过零点就是R波的对应点,然后在检测到R波的基础上检测出Q波与S波,再结合距离最大值法检测出QRS波群的起止点。并采用医学相关理论对检测结果进行优化,进一步去除错检点,补偿漏检点。最后利用MIT-BIH心率失常数据库中记录的数据对该算法进行验证,实验结果表明所提算法能准确检测QRS波群,平均检出率达到了99.97%。     

心电信号中R波的自适应检测方法

阐述一种检测心电图中R波的方法,通过小波阈值去噪及小波分解与重构去噪法去除心电信号的噪声,使用差分阈值法对心电信号中的R波进行自适应检测,其中闽值的选取具有自更新的特性。对R波多检与漏检的分析,设计相应的辅助策略检测R波。使用MIT-BIH标准库中的心电数据对本方法进行仿真验证,结果表明：本方法能够准确有效地检测出心电图中的R波。     

Feature extraction for ECG heartbeats using higher order statistics of WPD coefficients

This paper describes feature extraction methods using higher order statistics (HOS) of wavelet packet decomposition (WPD) coefficients for the purpose of automatic heartbeat recognition. The method consists of three stages. First, the wavelet package coefficients (WPC) are calculated for each different type of ECG beat. Then, higher order statistics of WPC are derived. Finally, the obtained feature set is used as input to a classifier, which is based on k-NN algorithm. The MIT-BIH arrhythmia database is used to obtain the ECG records used in this study. All heartbeats in the arrhythmia database are grouped into five main heartbeat classes. The classification accuracy of the proposed system is measured by average sensitivity of 90%, average selectivity of 92% and average specificity of 98%. The results show that HOS of WPC as features are highly discriminative for the classification of different arrhythmic ECG beats.     

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 of electrocardiogram beats using a fuzzy similarity index

Abstract: A new approach based on the computation of a fuzzy similarity index (FSI) is presented for the detection of electrocardiogram (ECG) beats. Four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat) obtained from the Physiobank database were analysed. The ECG signals were decomposed into time–frequency representations using the discrete wavelet transform and wavelet coefficients were calculated to represent the signals. The aim of the study is detection of ECG beats by the combination of wavelet coefficients and the FSI. Toward achieving this aim, fuzzy sets were obtained from the feature sets (wavelet coefficients) of the signals under study. The results demonstrated that the similarity between the fuzzy sets of the studied signals indicated the variabilities in the ECG signals. Thus, the FSI could discriminate the normal beat and the other three types of beats (congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat).     

心电医疗监护物联网关键技术研究*

针对物联网技术的发展,进行了心电医疗监护物联网感知层传感器节点软硬件设计,完成了基于NesC语言的组件结构化软件设计。在经典聚类路由协议LEACH之上提出了一种适用于心电医疗监护物联网感知层的改进型LEACH-SC算法,将感知层内簇头的分布进行优化,平衡簇的规模,在一定程度上解决簇头分布不均匀的问题。为保证心电医疗监护物联网应用层实时准确的心电诊断,提出了一种基于小波变换、希尔伯特变换和改进包络对心电信号进行变换的检测算法,实现了对QRS波群具体形态和位置的检测和识别,在检测到QRS波的基础上采用检测准则     

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。     

Detection of electrocardiogram signals using an efficient method

Automatic detection of electrocardiogram (ECG) signals is very important for clinical diagnosis of heart disease. This paper investigates the design of a three-step system for recognition of the five types of ECG beat. In the first step, stationary wavelet transform (SWT) is used for noise reduction of the electrocardiogram (ECG) signals. Feature extraction module extracts higher order statistics of ECG signals in combination with three timing interval features. Then hybrid Bees algorithm-radial basis function (RBF_BA) technique is used to classify the five types of electrocardiogram (ECG) beat. The suggested method can accurately classify and discriminate normal (Normal) and abnormal heartbeats. Abnormal heartbeats include left bundle branch block (LBBB), right bundle branch block (RBBB), atrial premature contractions (APC) and premature ventricular contractions (PVC). Finally, the classification capability of five different classes of ECG signals is attained over eight files from the MIT/BIH arrhythmia database. Simulation results show that classification accuracy of 95.79% for the first dataset (4000 beats) and an overall accuracy of detection of 95.18% are achieved over eight files from the MIT/BIH arrhythmia database.     

Automatic diagnosis of cardiac arrhythmia in electrocardiograms via multigranulation computing

The automatic and accurate arrhythmia diagnosis in the electrocardiogram (ECG) signals is significant for cardiac health. Typically, the arrhythmia diagnosis is automatically detected depending on single-lead signals or a simple combination of multilead signals from the ECG. However, it ignores the inter-lead correlation and the significance of different leads for different heart beats detection, which decreases the performance of arrhythmia diagnosis. In this paper, arrhythmia diagnosis is converted to a problem of multigranulation computing in the view of granular computing, and thus different lead signals can be captured to improve the effectiveness of abnormal heart beats detection. To this end, multilead ECG signals are firstly granulated into different fuzzy information granules by the fuzzy equivalence relation. An objective decision-making model based on fuzzy set theory is then proposed for describing and analyzing these granulated multilead ECG signals, which brings a self-adaptive and unsupervised decision making. As a result, the significance and correlation of different leads are analyzed by granularity selection and granular structures to make a better decision for arrhythmia diagnosis. Extensive experimental results show that the proposed algorithm can significantly improve the performance of arrhythmia diagnosis, especially better robustness to several types of cardiac arrhythmia.     

基于形态学和小波变换的心电信号去噪算法

在处理心电信号采集过程中混入的基线漂移、工频干扰及肌电干扰等噪声的过程中,小波变换取得了广泛的应用。针对小波算法的缺陷及不足,提出了一种基于数学形态学和小波阈值的混合算法。该算法利用非线性形态学滤波器滤除基线漂移,将获得的含高频噪声心电信号通过小波阈值算法进行处理,最后获得无噪声的ECG（心电）信号。采用MIT/BIH Arrhythmia Database中的数据对算法进行了验证,实现了三种主要干扰的滤除,本算法效果良好,为后续特征点的识别奠定了基础。     

Arrhythmia classification based on wavelet transformation and random forests

Cardiovascular disease accompanied by arrhythmia reduces an individual’s lifespan and health, and long term ECG monitoring would generate large amounts of data. Fortunately, arrhythmia classification assisted by computer science would greatly improve the efficiency of doctors’ diagnoses. However, due to individual differences, noise affecting the signal, the great variety of arrhythmias, and heavy computing workload, it is difficult to implement these advanced techniques for clinical context analysis. Thus, this paper proposes a comprehensive approach based on discrete wavelet and random forest techniques for arrhythmia classification. Specifically, discrete wavelet transformation is used to remove high-frequency noise and baseline drift, while discrete wavelet transformation, autocorrelation, principal component analysis, variances and other mathematical methods are used to extract frequency-domain features, time-domain features and morphology features. Furthermore, an arrhythmia classification system is developed, and its availability is verified that the proposed scheme can significantly be used for guidance and reference in clinical arrhythmia automatic classification.     

基于心电图的身份识别方法

针对当前心电图(ECG)身份识别中存在的小样本、多特征点检测问题,提出基于小波变换和动态时间规整(DTW)相结合的方法.利用小波变换对ECG信号进行预处理并提取R波峰值点,提取并保存肢导联QRS波及心拍模板,根据QRS波测试数据与各QRS波模板间的相关性分析以及阈值条件缩小身份识别范围,采用 DTW算法确定心拍测试数据...     

基于经验模式分解的R波识别及噪声消除

提出了一种新的心电信号R波识别方法,探讨了经验模式分解在心电信号R波识别领域的可行性,并结合该理论给出R波的识别算法,用MIT心电数据库中部分记录进行验证,取得了比较理想的效果.最后应用该理论处理心电信号噪声时,发现去噪的效果优于仅使用小波方法去噪.     

WinArrhythmia: A Windows based application for studying cardiac arrhythmias

We have developed Windows-based software for ECG training as a tool in teaching physiology. A standard user interface allows the user to choose which arrhythmia to review. The arrhythmia is drawn in real time with sound beeps synchronized to R waves. The system also presents a brief summary or multiple choice question corresponding to the arrhythmia. A ladder diagram shows how simulate the conduction system, which consisted of 4 modules characterized by 4 parameters: automaticity, refractory period, antegrade- and retrograde-conduction time. This system has proved both useful and effective for training medical students in ECG interpretation of arrhythmias.     

Cross-correlation technique for arrhythmia detection using PR and PP intervals

A combined, cross-correlation procedure for computerized arrhythmia detection is presented. It enables the classification of a wide range of arrhythmia and is based upon the determination of both P and QRS waves. The electrocardiogram (ECG) signal is recorded from external leads, digitized (10 bits of resolution) at 1.28 kHz and processed with a CDC 6600 computer. A normal beat is stored as a reference signal and the onsets of the P wave and the QRS complex are measured for determining the PR, PP, and RR intervals. The combined cross-correlation procedure is carried out between the template and each successive ECG waveform over two time intervals, the first of which includes the P wave and the second, the QRS complex. Prior to this correlation procedure each of the ECG waveforms is filtered through a nonrecursive digital bandpass filter (10 and 100 Hz for low and high cutoff frequencies, respectively). The cross-correlation functions are then calculated by means of a cross spectrum and Fast Fourier Transform algorithm. The maximum value of the normalized cross-correlation function and the time shift providing that value are searched for, allowing (1) determination of the similarity between the present beat and the normal reference beat, allowing for the discrimination of abnormal P and QRS shapes; (2) accurate measurement of the RR, PP, and PR intervals of this subsequent waveform. Applying this method enables a reliable detection of a wide variety of arrhythmia.     

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.     

ECG arrhythmia recognition via a neuro-SVM-KNN hybrid classifier with virtual QRS image-based geometrical features

In this study, a new supervised noise-artifact-robust heart arrhythmia fusion classification solution, is introduced. Proposed method consists of structurally diverse classifiers with a new QRS complex geometrical feature extraction technique.Toward this objective, first, the events of the electrocardiogram (ECG) signal are detected and delineated using a robust wavelet-based algorithm. Then, each QRS region and also its corresponding discrete wavelet transform (DWT) are supposed as virtual images and each of them is divided into eight polar sectors. Next, the curve length of each excerpted segment is calculated and is used as the element of the feature space. Discrimination power of proposed classifier in isolation of different Gold standard beats was assessed with accuracy 98.20%. Also, proposed learning machine was applied to 7 arrhythmias belonging to 15 different records and accuracy 98.06% was achieved. Comparisons with peer-reviewed studies prove a marginal progress in computerized heart arrhythmia recognition technologies.     

Adaptive wavelet network for multiple cardiac arrhythmias recognition

This paper proposes a method for electrocardiogram (ECG) heartbeat detection and recognition using adaptive wavelet network (AWN). The ECG beat recognition can be divided into a sequence of stages, starting with feature extraction from QRS complexes, and then according to characteristic features to identify the cardiac arrhythmias including the supraventricular ectopic beat, bundle branch ectopic beat, and ventricular ectopic beat. The method of ECG beats is a two-subnetwork architecture, Morlet wavelets are used to enhance the features from each heartbeat, and probabilistic neural network (PNN) performs the recognition tasks. The AWN method is used for application in a dynamic environment, with add-in and delete-off features using automatic target adjustment and parameter tuning. The experimental results used from the MIT-BIH arrhythmia database demonstrate the efficiency of the proposed non-invasive method. Compared with conventional multi-layer neural networks, the test results also show accurate discrimination, fast learning, good adaptability, and faster processing time for detection.