ECG Beat Research Articles

CHAOTIC FEATURE EXTRACTION AND NEURO-FUZZY CLASSIFIER FOR ECG SIGNAL CHARACTERIZATION

In this paper, a neuro-fuzzy network is employed to classify the ECG beats based on the extracted chaotic features. Six groups of ECG beats (MIT-BIH Normal Sinus rhythm, BIDMC congestive heart failure, CU ventricular tachyarrhythmia, MIT-BIH atrial fibrillation, MIT-BIH Malignant Ventricular Arrhythmia and MIT-BIH supraventricular arrhythmia) are characterized by the six chaotic parameters including the largest Lyapunov exponent and average of the Lyapunov spectrum (related to the chaoticity of the signal), time lag and embedding dimension (related to the phase space reconstruction) and correlation dimension and approximate entropy of the signal (related to the complexity of the signal). Finally, six structures of the neuro-fuzzy network (in terms of the type of fuzzy set, the number of fuzzy sets per variable and the number of learning epochs) were employed to perform the ECG beats classification based on all extracted features for two lengths of the signals. It was found that all respective chaotic features are discriminative and they improve the classification rate of ECG beats. Also, it is shown that a minimum length of the signal is needed for exhibitive feature extraction and for the higher lengths of the signal (in time) no significant improvement is achieved in feature extraction and calculations. The criteria for the classification task are considered as accuracy, specificity and sensitivity which all together comprehensively demonstrate the capability and performance of the classification. Some conclusions are drawn and they are discussed at the end of the paper.

ECG beat classification using particle swarm optimization and support vector machine

In this paper, we propose a novel ECG arrhythmia classification method using power spectral-based features and support vector machine (SVM) classifier. The method extracts electrocardiogram’s spectral and three timing interval features. Non-parametric power spectral density (PSD) estimation methods are used to extract spectral features. The proposed approach optimizes the relevant parameters of SVM classifier through an intelligent algorithm using particle swarm optimization (PSO). These parameters are: Gaussian radial basis function (GRBF) kernel parameter σ and C penalty parameter of SVM classifier. ECG records from the MIT-BIH arrhythmia database are selected as test data. It is observed that the proposed power spectral-based hybrid particle swarm optimization-support vector machine (SVMPSO) classification method offers significantly improved performance over the SVM which has constant and manually extracted parameter.

ECG beat classification using a cost sensitive classifier

In this paper, we introduce a new system for ECG beat classification using Support Vector Machines (SVMs) classifier with rejection. After ECG preprocessing, the QRS complexes are detected and segmented. A set of features including frequency information, RR intervals, QRS morphology and AC power of QRS detail coefficients is exploited to characterize each beat.An SVM follows to classify the feature vectors. Our decision rule uses dynamic reject thresholds following the cost of misclassifying a sample and the cost of rejecting a sample. Significant performance enhancement is observed when the proposed approach is tested with the MIT-BIH arrhythmia database. The achieved results are represented by the average accuracy of 97.2% with no rejection and 98.8% for the minimal classification cost.

Characterization of ECG beats from cardiac arrhythmia using discrete cosine transform in PCA framework

Electrocardiogram is the P-QRS-T wave representing the cardiac depolarization and re-polarization, recorded at the body surface. The subtle changes in amplitude and duration of these waves indicate various pathological conditions. It is very difficult to decipher minute changes in the ECG wave by naked eye. Hence a computer aided diagnosis tool to classify various cardiac diseases will assist the doctors in their ECG reading. In this paper, five types of ECG beats (ANSI/AAMI EC57:1998 standard) of MIT–BIH arrhythmia database were automatically classified. Our proposed methodology involves computation of Discrete Cosine Transform (DCT) coefficients from the segmented beats of ECG, which were then subjected for principal component analysis for dimensionality reduction. Then the clinically significant principal components were fed to (i) feed forward neural network, (ii) least square support vector machine with different kernel functions, and (iii) Probabilistic Neural Network (PNN) for automatic classification. We have obtained the highest average sensitivity of 98.69%, specificity of 99.91%, and classification accuracy of 99.52% with the developed knowledge based system. The developed system is clinically ready to deploy for mass screening programs.

089: Expanding the phenotype associated with a desmoplakin dominant mutation: Carvajal/Naxos syndrome associated with leukonychia and oligodentia

We report on a family with a rare form of cardiac and non-cardiac disease that shed new lights on the clinical spectrum associated with desomomal gene mutations, usually related to arrhythmogenic right ventricular cardiomyopathy. The 29-year-old patient presented with woolly hair from infancy and palmoplantar keratoderma, leuconychia (hands and feet) and oligodontia (agenesis of 4 teeth). Global cardiac biventricular involvement was present on echocardiography and MRI. Late potentials were present on signal-average ECG and ventricular premature beats (PVB) on Holter-ECG (>10/24h). A single-chamber prophylactic implantable cardioverter-defibrillator was implanted. The son of the patient had palmoplantar keratoderma, woolly hair, oligodontia (agenesis of 10 teeth) and brittle nails. At 10 years of age, a diagnosis of acute myocarditis was made (chest pain, cardiac troponin elevation, multiple isolated PVB, typical sign on MRI). RV infundibulum was dilated on Echocardiography. Evolution was unremarkable after medical treatment. A heterozygous missense mutation (c.1691C>T, p. Thr564Ile) was identified in the desmoplakin gene (both in father and son). No additional mutation was identified in other desomosomal genes. Carvajal/Naxos syndromes are usually recessive and characterized by wooly hair, palmoplantar keratoderma and a cardiomyopathy. We report on a family with unique features associated with a new mutation of desmoplakin gene. First, mode of inheritance is autosomal dominant and not recessive. Second, clinical expression is associating not only the usual Carvajal/Naxos syndrome but also oligodentia and leuconychia. This report (i) extends the phenotype associated with desmosomal gene mutations, (ii) demonstrates that leuconychia and oligodentia may be associated with severe cardiomyopathy and may therefore require systematic cardiac examination, (iii) suggests that desmoplakin gene is involved in the normal and abnormal development of teeth and nails.

A model presented for classification ECG signals base on Case-Based Reasoning

Early detection of heart diseases/abnormalities can prolong life and enhance the quality of living through appropriate treatment; thus classifying cardiac signals will be helped to immediate diagnosing of heart beat type in cardiac patients. The present paper utilizes the case base reasoning (CBR) for classification of ECG signals. Four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat and atrial fibrillation beat) obtained from the PhysioBank database was classified by the proposed CBR model. The main purpose of this article is classifying heart signals and diagnosing the type of heart beat in cardiac patients that in proposed CBR (Case Base Reasoning) system, Training and testing data for diagnosing and classifying types of heart beat have been used. The evaluation results from the model are shown that the proposed model has high accuracy in classifying heart signals and helps to clinical decisions for diagnosing the type of heart beat in cardiac patients which indeed has high impact on diagnosing the type of heart beat aided computer.

Improved cardiac gating at 3T with the “3D-QRS” method utilizing MRI-compatible 12-lead ECGs

Background Blood is rapidly ejected from the left ventricle during early systole, travels in the aortic arch perpendicular to the MRI’s main field, thus generating a Magnetohydrodynamic (MHD) voltage which is larger than the real ECG QRS complex in high field MRI. The MHD voltage (VMHD) is severally irregular in patients with arrhythmia, since arrhythmic ECG beats are interleaved between successive sinus rhythm (SR) beats. The VMHD overlay in ECG traces can result in intermittent QRS detection, leading to blurred images and longer scan times. Since accurate gating is essential for successful cardiac imaging, we developed a “3D-QRS” method for real-time detection of the QRS complex based on 12-lead ECG traces acquired inside the MRI. We validated this method at 3T in patients with Premature Ventricular Contractions (PVCs), Atrial Fibrillation (AF) and in an exercising athlete with time-varying heart rate.

A hybrid algorithm based on KFCM-HACO-FAPSO for clustering ECG beat

A hybrid algorithm based on KFCM-HACO-FAPSO for clustering ECG beat

Interpretation of Normal and Pathological ECG Beats using Multiresolution Wavelet Analysis

Interpretation of Normal and Pathological ECG Beats using Multiresolution Wavelet Analysis

A new ECG beat clustering method based on kernelized fuzzy c-means and hybrid ant colony optimization for continuous domains

The kernelized fuzzy c-means algorithm uses kernel methods to improve the clustering performance of the well known fuzzy c-means algorithm by mapping a given dataset into a higher dimensional space non-linearly. Thus, the newly obtained dataset is more likely to be linearly seprable. However, to further improve the clustering performance, an optimization method is required to overcome the drawbacks of the traditional algorithms such as, sensitivity to initialization, trapping into local minima and lack of prior knowledge for optimum paramaters of the kernel functions. In this paper, to overcome these drawbacks, a new clustering method based on kernelized fuzzy c-means algorithm and a recently proposed ant based optimization algorithm, hybrid ant colony optimization for continuous domains, is proposed. The proposed method is applied to a dataset which is obtained from MIT–BIH arrhythmia database. The dataset consists of six types of ECG beats including, Normal Beat (N), Premature Ventricular Contraction (PVC), Fusion of Ventricular and Normal Beat (F), Artrial Premature Beat (A), Right Bundle Branch Block Beat (R) and Fusion of Paced and Normal Beat (f). Four time domain features are extracted for each beat type and training and test sets are formed. After several experiments it is observed that the proposed method outperforms the traditional fuzzy c-means and kernelized fuzzy c-means algorithms.

HEARTBEAT CLASSIFICATION USING SUPPORT VECTOR MACHINES (SVMs) WITH AN EMBEDDED REJECT OPTION

In this paper, we introduce a new system for ECG beat classification using support vector machines classifier with a double hinge loss. The proposed classifier rejects samples that cannot be classified with enough confidence. Specifically in medical diagnoses, the consequence of a wrong classification can be so harmful that it is convenient to reject such sample. After ECG preprocessing, feature selection and extraction, our decision rule uses dynamic reject thresholds according to the cost of rejecting or misclassifying a sample. Significant performance enhancement is observed when the proposed approach is tested with the MIT-BIH arrythmia database. The achieved results are represented by the error reject tradeoff. We obtained 98.2% of sensitivity with no rejection and more than 99% of sensitivity for the optimal classification cost being competitive to other published studies.

Classification of ECG Signals Using 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) 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, they are the k-nearest neighbor classifier (kNN) and the radial basis function neural network classifier (RBF), 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 to traditional classifiers.

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.

Selection of effective features for ECG beat recognition based on nonlinear correlations

The objective of this study is to develop feature selectors based on nonlinear correlations in order to select the most effective and least redundant features from an ECG beat classification system based on higher order statistics of subband components and a feed-forward back-propagation neural network, denoted as HOS-DWT-FFBNN. Three correlation-based filters (NCBFs) are proposed. Two of them, NCBF1 and NCBF2, apply feature-feature correlation to remove redundant features prior to the feature selection process based on feature-class correlation. The other, SUFCO, skips the redundancy reduction process and selects features based only on feature-class correlation. The performance of these filters is compared to another commonly used nonlinear feature selection method, Relief-F. The discriminality and redundancy of the retained features are evaluated quantitatively. The performance of the most effective NCBF is compared with that of the linear correlation-based filter (LCBF) and other representative heartbeat classifiers in the literature. The results demonstrate that the two NCBFs based on both feature-feature and feature-class correlation methods, i.e. NCBF1 and NCBF2, outperform the other two methods, i.e. SUFCO and Relief-F. An accuracy of as high as 96.34% can be attained with as few as eight features. When tested with statistical methods, the retained features selected by the NCBF1/NCBF2 approach are demonstrated to be more discriminative and less redundant when compared with those features selected by other methods. When compared with LCBF and other heartbeat classifiers in the literature, the proposed NCBF1/NCBF2 approach in conjunction with the HOS-DWT-FFBNN structure outperform them with improved performance that allows discrimination of more beat types and fewer feature dimensions. This study demonstrates the effectiveness and superiority of the proposed approach for ECG beat recognition.

Identification of ECG beats from cross-spectrum information aided learning vector quantization

This work describes the development of a computerized medical diagnostic tool for heart beat categorization. The main objective is to achieve an accurate, timely detection of cardiac arrhythmia for providing appropriate medical attention to a patient. The proposed scheme employs a feature extractor coupled with an Artificial Neural Network (ANN) classifier. The feature extractor is based on cross-correlation approach, utilizing the cross-spectral density information in frequency domain. The ANN classifier uses a Learning Vector Quantization (LVQ) scheme which classifies the ECG beats into three categories: normal beats, Premature Ventricular Contraction (PVC) beats and other beats. To demonstrate the generalization capability of the scheme, this classifier is developed utilizing a small training dataset and then tested with a large testing dataset. Our proposed scheme was employed for 40 benchmark ECG files of the MIT/BIH database. The system could produce classification accuracy as high as 95.24% and could outperform several competing algorithms.

A wavelet optimization approach for ECG signal classification

Wavelets have proved particularly effective for extracting discriminative features in ECG signal classification. In this paper, we show that wavelet performances in terms of classification accuracy can be pushed further by customizing them for the considered classification task. A novel approach for generating the wavelet that best represents the ECG beats in terms of discrimination capability is proposed. It makes use of the polyphase representation of the wavelet filter bank and formulates the design problem within a particle swarm optimization (PSO) framework. Experimental results conducted on the benchmark MIT/BIH arrhythmia database with the state-of-the-art support vector machine (SVM) classifier confirm the superiority in terms of classification accuracy and stability of the proposed method over standard wavelets (i.e., Daubechies and Symlet wavelets).

ECG classification and abnormality detection using cascade forward neural network

Electrical activity of the heart is called as electrocardiogram i.e. ECG. Arrhythmias are among the most common ECG abnormalities. ECGs provide lots f information about heart abnormalities. The diagnosis depends upon the physician and it varies from physician to physician and also depends upon the experience of the physician. Previously many techniques were tried for analysis and automisation of the analysis. This paper describes the use of MATLAB based artificial neural network tools for ECG analysis for finding out whether the ECG is normal or abnormal and if it is abnormal, what is the abnormality. There are various arrhythmia like Ventricular premature beats, asystole, couplet, bigeminy, fusion beats etc. To classify this, various weighted neural networks were tried with different algorithms. They were provided training inputs from the standard MIT-BIH Arrhythmia database and tested by providing unknown patient data from the same database. The results obtained with different networks and different algorithms are compared, it is found that to identify whether the ECG beat is normal or abnormal, cascade forward back network algorithm has shown 99.9 % correct classification. These results are compared with previous neural network techniques and found that method proposed in this paper gives best results.

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.

ECG beat classification using features extracted from Teager energy functions in time and frequency domains

It is hypothesised that a key characteristic of ECG signal is its non-linear dynamic behaviour and that the non-linear component changes more significantly between normal and arrhythmia conditions than the linear component. This study makes an attempt to analyse ECG beats from an energy point of view by accounting for the features derived from non-linear component in time and frequency domains using Teager energy operator (TEO). The key feature of TEO is that it models the energy of the source that generated the signal rather than the energy of the signal itself. Hence any deviations in the regular rhythmic activity of the heart get reflected in the Teager energy function. To show the validity of appropriate choice of features, t-tests and scatter plot are used. The t-tests show significant statistical differences and scatter plot of mean of Teager energy in time domain against mean of Teager energy in frequency domain for the ECG beats evaluated on selected Manipal Institute of Technology–Beth Israel Hospital (MIT–BIH) database, which reveals an excellent separation of the features into five different classes: normal, left bundle branch block, right bundle branch block, premature ventricular contraction and paced beats. The neural network results achieved through only two non-linear features exhibit an average accuracy that exceeds 95%, average sensitivity of about 80% and average specificity of almost 100%.

A robust method for diagnosis of morphological arrhythmias based on Hermitian model of higher-order statistics

BackgroundElectrocardiography (ECG) signal is a primary criterion for medical practitioners to diagnose heart diseases. The development of a reliable, accurate, non-invasive and robust method for arrhythmia detection could assists cardiologists in the study of patients with heart diseases. This paper provides a method for morphological heart arrhythmia detection which might have different shapes in one category and also different morphologies in relation to the patients. The distinctive property of this method in addition to accuracy is the robustness of that, in presence of Gaussian noise, time and amplitude shift.MethodsIn this work 2nd, 3rd and 4th order cumulants of the ECG beat are calculated and modeled by linear combinations of Hermitian basis functions. Then, the parameters of each cumulant model are used as feature vectors to classify five different ECG beats namely as Normal, PVC, APC, RBBB and LBBB using 1-Nearest Neighborhood (1-NN) classifier. Finally, after classifying each model, a final decision making rule apply to these specified classes and the type of ECG beat is defined.ResultsThe experiment was applied for a set of ECG beats consist of 9367 samples in 5 different categories from MIT/BIH heart arrhythmia database. The specificity of 99.67% and the sensitivity of 98.66% in arrhythmia detection are achieved which indicates the power of the algorithm. Also, the accuracy of the system remained almost intact in the presence of Gaussian noise, time shift and amplitude shift of ECG signals.ConclusionsThis paper presents a novel and robust methodology in morphological heart arrhythmia detection. The methodology based on the Hermite model of the Higher-Order Statistics (HOS). The ability of HOS in suppressing morphological variations of different class-specific arrhythmias and also reducing the effects of Gaussian noise, made HOS, suitable for detection morphological heart arrhythmias. The proposed method exploits these properties in conjunction with Hermitian model to perform an efficient and reliable classification approach to detect five morphological heart arrhythmias. And the time consumption of this method for each beat is less than the period of a normal beat.