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).     

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.     

A fast expert system for electrocardiogram arrhythmia detection

Abstract: A fast expert system for electrocardiogram (ECG) arrhythmia detection has been designed in this study. Selecting proper wavelet details, the ECG signals are denoised and beat locations are detected. Beat locations are later used to locate the peaks of the individual waves present in each cardiac cycle. Onsets and offsets of the P and T waves are also detected. These are considered as ECG features which are later used for arrhythmia detection utilizing a novel fuzzy classifier. Fourteen types of arrhythmias and abnormalities can be detected implementing the proposed procedure. We have evaluated the algorithm on the MIT–BIH arrhythmia database. Application of the wavelet filter with the scaling function which closely resembles the shape of the ECG signal has been shown to provide precise results in this study.     

A modified mixture of experts network structure for ECG beats classification with diverse features

This paper illustrates the use of modified mixture of experts (MME) network structure to guide model selection for classification of electrocardiogram (ECG) beats with diverse features. The MME is a modular neural network architecture for supervised learning. Expectation-maximization (EM) algorithm was used for training the MME so that the learning process is decoupled in a manner that fits well with the modular structure. The wavelet coefficients and Lyapunov exponents of the ECG signals were computed and statistical features were calculated to depict their distribution. The statistical features, which were used for obtaining the diverse features of the ECG signals, were then input into the MME network structure for training and testing purposes. We explored the ability of designed and trained MME network structure, combined with wavelet preprocessing (computing wavelet coefficients) and nonlinear dynamics tools (computing Lyapunov exponents), to discriminate five types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat, partial epilepsy beat) obtained from the Physiobank database. The MME achieved accuracy rates which were higher than that of the mixture of experts (ME) and feedforward neural network models (multilayer perceptron neural network—MLPNN). The proposed MME approach can be useful in classifying long-term ECG signals for early detection of heart diseases/abnormalities.     

Effective and efficient detection of premature ventricular contractions based on variation of principal directions

Classification of electrocardiogram (ECG) data stream is essential to diagnosis of critical heart conditions. It is vital to accurately detect abnormality in the ECG in order to prevent possible beginning of life-threatening cardiac symptoms. In this paper, we focus on identifying premature ventricular contraction (PVC) which is one of the most common heart rhythm abnormalities. We use “Replacing” strategy to check the effects of each individual heartbeat on the variation of principal directions. Based on this idea, an online PVC detection method is proposed to classify the new arriving PVC beats in the real-time and online manner. The proposed approach is tested on the MIT-BIH arrhythmia database (MIT-BIH-AR). The PVC detection accuracy was 98.77%, with the sensitivity and positive predictivity of 96.12% and 86.48%, respectively. These results are an improvement on previous reported results for PVC detection. In addition, our proposed method is effective in terms of computation time. The average execution time of our proposed method was 3.83 s for a 30 min ECG recording. It shows the capability of the classifier to detect abnormal PVCs in online manner.     

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%.     

Electrocardiogram beat classification via coupled boosting by filtering and preloaded mixture of experts

The role of electrocardiogram (ECG) as a noninvasive technique for detecting and diagnosing cardiac problems cannot be overemphasized. This paper introduces a modified version of boosted mixture of experts for the classification of three types of ECG beats. Our two-step preloading procedure, along noise injection, also regarded as smoothing regularization, proved to be a promising, effective, and safe means of classifying arrhythmias. The proposed model, according to the nature of implementation, is called coupled boosting by filtering and preloaded mixture of experts. The experimental results show our proposed method have better classification rate against other compared methods. Comparative evaluation is accomplished with ECG signals from MIT–BIH arrhythmia database.     

WebECG: A novel ECG simulator based on MATLAB Web Figure

Electrocardiogram is a signal containing information about the condition and operation of heart. Nowadays, many heart diseases can be efficiently diagnosed using these signals. However, a proper recognition and classification of the heart signals are essential requirement for the diagnosis of heart diseases. In this study, emphasizing on this requirement, a new ECG simulator based on MATLAB Web Figure called WebECG is designed and implemented to facilitate the education on ECG signals. Advanced flexibility and good visualization capabilities including 3-dimension view, zoom and move on ECG graphics are provided by WebECG. The users are able to plot ECG signals with different parameters, to plot the ECGs of nine arrhythmia types. Furthermore, WebECG is capable to add three different noises to ECG and to plot/analyze long-term ECGs. These properties of the WebECG support efficient web-based education of ECG signals.     

基于数学形态学及支持向量机的心率失常识别

为实现对不同类型的心电图自动分析,研究并提出了一种顺序筛选极大值的R波定位算法,并采用支持向量机(SVM)进行最后的心律失常心拍识别。定位算法以数学形态学为基础,结合心电图自身特点,定义R波筛选区间,避免了传统算法中的阈值选择;定位R波峰后以R波峰为中心提取不同类型的心率失常的心拍,选择径向基(RBF)支持向量机进行识别分类。使用MIT-BIH心率失常数据库文件进行实验仿真,结果表明,算法对含不同类型心拍的心电图R波峰正确检测率较高(99.36%),学习后的SVM能有效识别早搏、房颤、束支传导阻滞、正常等不用类型心拍,总体识别率达到99.75%。     

基于深度学习和模糊C均值的心电信号分类方法

针对长时海量心电信号自动分类系统中,心电专家诊断费时、费力和成本高,心电信号形态复杂导致特征提取困难,异常诊断模型适应性差、准确度低等问题,本文提出一种基于深度学习和模糊C均值的心电信号分类方法.该方法主要包括心电信号降噪预处理、心电信号分段和采样点统一化、无监督心跳特征学习、模糊C均值分类4个步骤,给出了模糊C均值深度信念网络FCMDBN模型结构和学习分类算法.仿真实验基于MIT-BIH心率异常数据库表明,与基于传统心电特征人工设计的分类方法相比,本文提出的信号诊断方法具有较高的适应性和准确度.     

Evaluation of bagging ensemble method with time-domain feature extraction for diagnosing of arrhythmia beats

We explore the effect of using bagged decision tree (BDT) as an ensemble learning method with proposed time-domain feature extraction methods on electrocardiogram (ECG) arrhythmia beat classification comparing with single decision tree (DT) classifier. RR interval is the main property which defines irregular heart rhythm, and its ratio to the previous value and difference from mean value are used as morphological feature extraction methods. Form factor, its ratio to the previous value and difference from mean value are used to express ECG waveform complexity. In addition, skewness and second-order linear predictive coding coefficients are added to the feature vector of 56,569 ECG heart beats obtained from MIT–BIH arrhythmia database as time-domain feature extraction methods. The quarter of ECG heart beat samples are used as test data for DT and BDT. The performance measures of these classifiers are evaluated using the metrics such as accuracy, sensitivity, specificity and Kappa coefficient for both classifiers, and the performance of BDT classifier is examined for number of base learners up to 75. The BDT results in more predictive performance than DT according to the performance measures. BDT with 69 base learners has 99.51 % of accuracy, 97.50 % of sensitivity, 99.80 % of specificity and 0.989 of Kappa coefficient while DT gives 98.78, 96.05, 99.57 and 0.975 %, respectively. These metrics show that the suggested BDT increases the numbers of successfully identified arrhythmia beats. Moreover, BDT with at least three base learners has higher distinguishing capability than DT.     

Classification of electrocardiogram signals with support vector machines and extreme learning machine

An Electrocardiogram or ECG is an electrical recording of the heart and is used in the investigation of heart disease. This ECG can be classified as normal and abnormal signals. The classification of the ECG signals is presently performed with the support vector machine. The generalization performance of the SVM classifier is not sufficient for the correct classification of ECG signals. To overcome this problem, the ELM classifier is used which works by searching for the best value of the parameters that tune its discriminant function and upstream by looking for the best subset of features that feed the classifier. The experiments were conducted on the ECG data from the Physionet arrhythmia database to classify five kinds of abnormal waveforms and normal beats. In this paper, a thorough experimental study was done to show the superiority of the generalization capability of the Extreme Learning Machine (ELM) that is presented and compared with support vector machine (SVM) approach in the automatic classification of ECG beats. In particular, the sensitivity of the ELM classifier is tested and that is compared with SVM combined with two classifiers, and they are the k-nearest Neighbor Classifier and the radial basis function neural network classifier, with respect to the curse of dimensionality and the number of available training beats. The obtained results clearly confirm the superiority of the ELM approach as compared with traditional classifiers.     

基于卷积核补偿ECG检测分类算法

传统ECG检测分类算法通常分成检测、分类两个步骤,本文提出一种基于卷积核补偿ECG检测分类的新算法,可以将ECG的检测和识别合并成一步完成,文章对LTST数据库的ECG数据进行了该算法的验证,说明该算法完全可以实现ECG的检测和分类。     

Application of principal component analysis to ECG signals for automated diagnosis of cardiac health

Electrocardiogram (ECG) is the P, QRS, T wave indicating the electrical activity of the heart. The subtle changes in amplitude and duration of ECG cannot be deciphered precisely by the naked eye, hence imposing the need for a computer assisted diagnosis tool. In this paper we have automatically classified five types of ECG beats of MIT-BIH arrhythmia database. The five types of beats are Normal (N), Right Bundle Branch Block (RBBB), Left Bundle Branch Block (LBBB), Atrial Premature Contraction (APC) and Ventricular Premature Contraction (VPC). In this work, we have compared the performances of three approaches. The first approach uses principal components of segmented ECG beats, the second approach uses principal components of error signals of linear prediction model, whereas the third approach uses principal components of Discrete Wavelet Transform (DWT) coefficients as features. These features from three approaches were independently classified using feed forward neural network (NN) and Least Square-Support Vector Machine (LS-SVM). We have obtained the highest accuracy using the first approach using principal components of segmented ECG beats with average sensitivity of 99.90%, specificity of 99.10%, PPV of 99.61% and classification accuracy of 98.11%. The system developed is clinically ready to deploy for mass screening programs.     

Detecting variabilities of ECG signals by Lyapunov exponents

An approach based on the consideration that electrocardiogram (ECG) signals are chaotic signals was presented for automated diagnosis of electrocardiographic changes. This consideration was tested successfully using the nonlinear dynamics tools, like the computation of Lyapunov exponents. Multilayer perceptron neural network (MLPNN) architectures were formulated and used as basis for detection of variabilities of ECG signals. Four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat) obtained from the Physiobank database were classified. The computed Lyapunov exponents of the ECG signals were used as inputs of the MLPNNs trained with backpropagation, delta-bar-delta, extended delta-bar-delta, quick propagation, and Levenberg–Marquardt algorithms. The performances of the MLPNN classifiers were evaluated in terms of classification accuracies. The results confirmed that the MLPNN trained with the Levenberg–Marquardt algorithm has potential in detecting the variabilities of the ECG signals (total classification accuracy was 95.00%).     

基于U-NET网络的心律失常信号识别算法研究

在心电信号心律失常自动识别系统中,针对心电信号形态复杂导致特征提取困难、自动分类模型准确度低、现实应用性差的问题,设计了一种基于U-NET全卷积神经网络的心电信号语义分割的识别分类方法。该方法通过全卷积神经网络的编码运算规则,将心电信号切片数据作为输入,标签地图作为输出,可划分出信号片段中的心拍位置与类别。仿真结果表明：该方法在正常窦性搏动、左束支传导阻滞、右束支传导阻滞、房性早搏和室性早搏五分类问题中取得较高准确率,实现了对心律失常信号的有效识别。     

一种多导联QRS波实时检测算法

针对多导联心电监护仪对QRS波的分析需求,提出一种多导联QRS波实时检测算法。对原始心电图信号进行工频滤波和低通滤波处理,将各导联按照单导联预检波规则进行QRS波判别,通过决策融合多个导联的判别结果得到最终判别结果。在圣彼得堡INCART 12导联心率失常数据库上的验证结果表明,该算法的平均识别率和准确率分别为99.88%和99.73%。     

Cardiac health diagnosis using data fusion of cardiovascular and haemodynamic signals

The electrocardiogram (ECG) is a representative signal containing useful information about the condition of the heart. The shape and size of the P-QRS-T wave, the r-r interval, etc., may help to identify the nature of disease afflicting the heart. However, human observer cannot directly monitor these subtle details and it is difficult to evaluate the cardiac health using ECG alone. Hence, the fusion of ECG, blood pressure, saturated oxygen content and respiratory data for achieving improved clinical diagnosis of patients in cardiac care units. In this study, a computer based analysis and display of the heterogeneous signals for the detection of life threatening states is demonstrated using fuzzy logic based data fusion. And to evaluate the severity of the disease a new parameter, deterioration index is proposed and results are tabulated for various cases.     

窦性心律失常心电序列的可视化研究

对心电信号序列与心血管疾病之间存在关系的探索是研究心脏病临床诊断的一类经典论题。心电图是检测心脏病的重要工具,目前已采集到长期的批量数据,对其进行处理和判别具有实际意义。利用变值心电测量系统,对窦性心律T波改变这一特殊心电数据和正常心电序列进行处理形成2D散点图谱,以可视化的形式展示这两类心电信号的分布特征和异同[1],与传统心电图相比,所提方法具有直观透明易懂的特点;同时也列举了不同测量变量值情况下的心电序列可视化结果。     

Improving Twin Support Vector Machine Based on Hybrid Swarm Optimizer for Heartbeat Classification

The computer-aided diagnosis system is used to reduce the high mortality rate among heart patients through detecting cardiac diseases at an early stage. Since the process of detecting the cardiac heartbeat is a hard task because of the human eye cannot be distinguished between the variations in electrocardiogram (ECG) signals due to they are very small. There are several machine learning approaches are applied to improve the performance of detecting the heartbeats, however, these methods suffer from some limitations such as high time computational and slow convergence. To avoid these limitations, this paper proposed an ECG heartbeat classification approach, called Swarm-TWSVM, that combined twin support vector machines (TWSVMs) with the hybrid between the particle swarm optimization with gravitational search algorithm (PSOGSA). Also, the empirical mode decomposition (EMD) has been applied for the ECG noise removing, and feature extraction, then PSOGSA was used to find the optimal parameters of TWSVM to improve the classification process. The experiments were performed using the MIT-BIH arrhythmia database and results show that the Swarm- TWSVM gives better accuracy than TWSVM 99.44 and 85.87%, respectively.