Beat Classification Research Articles

Neighborhood rough set based ECG signal classification for diagnosis of cardiac diseases

Cardiac diseases are one of the foremost reasons of mortality. Hence, the early detection of cardiac diseases based on electrocardiogram (ECG) is important for delivering appropriate and timely treatment to the heart patients and it is increasing the heart patient’s survival. Recent trends in clinical decision making systems appeal automation in ECG signal processing and beat classification. Automatic beat classification is a significant method to support clinical specialists to categorize arrhythmia signals in ECG recording. The main objective of this paper is to construct novel automatic classification system for analysis of ECG signal and decision making purposes. The proposed method involves three main parts: De-noising, feature extraction and classification. Initially, discrete wavelet transform (DWT) is applied before classification for signal De-noising and feature extraction. In this work, neighborhood rough set is applied to classify the ECG signals into normal and four abnormal heart beats. The presence of neighborhood rough set classification algorithm (NRSC) produces very exciting recognition and classification abilities through a wide range of biomedical signal processing. The experimental analysis of the proposed NRSC algorithm is compared with the multi-layered perceptron, decision table, Naive Bayes and J48 classification algorithms. Here, the performance of classification algorithms has been evaluated in terms of sensitivity, specificity, Positive predictive value, negative predictive value, false predictive value, Matthews’s correlation coefficients, F-measure, Folke–Mallows Index and Kulcznski Index. The acquired results showed that the proposed algorithm attained 99.32 % of the classification accuracy using NRSC and DWT. Results indicated that the performance of this proposed NRSC classification method was remarkably superior to that of other classification techniques.

An efficient method for ECG beat classification and correction of ectopic beats

The analysis of the surface Electrocardiogram (ECG) is the most extended non-invasive technique in cardiological diagnosis. The ectopic beats are heart beats remarkably different to the normal beat morphology that provoke serious disturbances in electrocardiographic analysis. These beats are very common in atrial fibrillation (AF), causing important residua when ventricular activity has to be removed for atrial activity (AA) analysis. These beats may occur in both normal subjects and patients with heart disease, and their presence represents an important source of error which must be handled before any other analysis. In this work, a method is proposed to cancel out ectopics by classification between normal and abnormal beats. The systems is based on Radial Basis Function Neural Network (RBFNN). This new approach is compared to state-of-the-art techniques for the ectopic classification and cancellation in the MIT database. The results clearly demonstrated the improved ECG beats classification accuracy compared with other alternatives and a very accurate reduction of ectopic beats together with low distortion of the QRST complex.

Arrhythmia Detection Using Wigner-Ville Distribution Based Neural Network

Different techniques have been used by the researchers in recent years to detect arrhythmias from electrocardiogram(ECG) signal. In this paper we have used Wigner-Ville time-frequency energy distribution(WVD) and neural network to classify four different ECG beats. These four beats are Normal(N), Left Bundle Branch Block(L), Right Bundle Branch Block(R) and Ventricular Premature Contraction(V). The 1-D wall slice from the WVD of ECG signal is considered as the features to train the Back propagation Neural network(BPNN) for arrhythmia beat classification. ECG signal samples from MIT-BIH arrhythmia database are used. Two different testing datasets are considered to evaluate the performance of the technique. The experimental results achieved a maximum and minimum accuracy of 99.77% and 87.50% respectively.

ECG Beat Classification Using Evidential K -Nearest Neighbours

The Electrocardiogram (ECG) signal provides a useful non-interventional method for identifying cardiac arrhythmias. In this paper, we look at automatic ECG beat classification into 2 categories-Normal and Premature ventricular contraction using Dempster Shafer Theory (DST). In biomedical signal classification problems, the cost of making an erroneous decision can be high. Deferring a decision rather than taking a wrong decision might be beneficial. This is done by using the evidential k nearest neighbours (EKNN) approach which is based on Dempster Shafer Theory for classifying the ECG beats. RR interval features are used. Analysis is done on the MIT-BIH database. Performance evaluation is done by considering error rates. Performance of EKNN is compared with traditional k nearest neighbours (maximum voting) approach. Effect of training datasize is assessed by using training sets of varying sizes. The EKNN based classification system is shown to consistently outperform the KNN based classification system.

A knowledge-based real time embedded platform for arrhythmia beat classification

This paper aims for accurate classification of ECG beats in real-time. The design, implementation and results of FPGA oriented SOC based embedded platform in laboratory experimental setup is presented. The platform is designed using high-level systematic approach having the capability of real-time classification of ECG beats at the place of patients with high accuracy which will be helpful to enhance the healthcare for cardiovascular diseases. The algorithm involves the integration of the R-peak detection algorithm and Rulebased approach to classify four generic heartbeat classes namely Normal, PVC, Ventricular Fibrillation/Ventricular Flutter, 2° heart block beat. The aforesaid algorithms are performed in software on an integrated TSK3000A processor core (IP cores used) and implemented in hardware targeting FPGA (Xilinx Spartan 3AN). The developed platform is validated by generating real-time ECG beats using MIT-BIH arrhythmia database. The performance of the proposed implementation is evaluated in terms of sensitivity, specificity, positive predictivity and accuracy which is 97.72%, 99.09%, 96.46% and 97.96% respectively. The proposed hardware/software implementation has yielded improved results in comparison to the existing methodologies implemented on the FPGA platforms.

An Effective Classification of Heart Rate Data using PSO-FCM Clustering and Enhanced Support Vector Machine

Background/Objectives: Heart Rate Variability is an essential feature which decides the condition of human heart. ECG is used as diagnostic tool to access the electrical function of the heart. Methods/Statistical Analysis: The nine linear and nonlinear features are derived from the HRV signals. The feature extraction is carried out with the help of Particle Swarm Optimization (PSO) for data reduction. In proposed scheme Fuzzy C-Means (FCM) clustering and classifier integrated to enhance the accuracy result for ECG beat classification. Findings: The Enhanced SVM classifier classifies the heart rate data. Enhanced SVM classifier groups the linear and non-linear parameters as inputs, which are derived from the HRV signal. The denoise signals are classified and identifies the pattern for better classification of ECG signal. Application/Improvements: The proposed scheme is experimented with the assistance of the most commonly used MIT-BIH arrhythmia database and adequate results were obtained with an accuracy level of 98.38% than the other well-known approaches.

An Automatic Subject-Adaptable Heartbeat Classifier Based on Multiview Learning.

In this paper, a novel subject-adaptable heartbeat classification model is presented, in order to address the significant interperson variations in ECG signals. A multiview learning approach is proposed to automate subject adaptation using a small amount of unlabeled personal data, without requiring manual labeling. The designed subject-customized models consist of two models, namely, general classification model and specific classification model. The general model is trained using similar subjects out of a population dataset, where a pattern matching based algorithm is developed to select the subjects that are "similar" to the particular test subject for model training. In contrast, the specific model is trained mainly on a small amount of high-confidence personal dataset, resulting from multiview-based learning. The learned general model represents the population knowledge, providing an interperson perspective for classification, while the specific model corresponds to the specific knowledge of the subject, offering an intraperson perspective for classification. The two models supplement each other and are combined to achieve improved personalized ECG analysis. The proposed methods have been validated on the MIT-BIH Arrhythmia Database, yielding an average classification accuracy of 99.4% for ventricular ectopic beat class and 98.3% for supraventricular ectopic beat class, which corresponds to a significant improvement over other published results.

ECG heart beat classification method based on modified ABC algorithm

Electrocardiogram is the most commonly used tool for the diagnosis of cardiologic diseases. In order to help cardiologists to diagnose the arrhythmias automatically, new methods for automated, computer aided ECG analysis are being developed. In this paper, a Modified Artificial Bee Colony (MABC) algorithm for ECG heart beat classification is introduced. It is applied to ECG data set which is obtained from MITBIH database and the result of MABC is compared with seventeen other classifier's accuracy.In classification problem, some features have higher distinctiveness than others. In this study, in order to find higher distinctive features, a detailed analysis has been done on time domain features. By using the right features in MABC algorithm, high classification success rate (99.30%) is obtained. Other methods generally have high classification accuracy on examined data set, but they have relatively low or even poor sensitivities for some beat types. Different data sets, unbalanced sample numbers in different classes have effect on classification result. When a balanced data set is used, MABC provided the best result as 97.96% among all classifiers.Not only part of the records from examined MITBIH database, but also all data from selected records are used to be able to use developed algorithm on a real time system in the future by using additional software modules and making adaptation on a specific hardware.

Hybrid Features and Classifier for Classification of ECG Signal

In this research, we have proposed an efficient technique to classify beat from ECG database. The proposed technique is composed into three stages, 1) pre processing 2) Hybrid feature extraction 3) hybrid feature classifier. The beat signals are initially taken from the physiobank ATM and in the pre-processing stage the beat signals are made suitable for feature extraction. For efficient feature extraction we use hybrid feature extractor. The hybrid feature extraction is done in three steps, i) Morphological based feature extraction ii) Haar wavelet based feature extraction iii) Tri-spectrum based feature extraction. Once the feature is extracted the hybrid classifier is used to classify the beat signal as normal or abnormal. Beat classification studies are conducted on the MIT-BIH Arrhythmia Database using three efficient features like as morphological, wavelet and trispectrum. The beat classification system based morphological information gives an accuracy of 68%, wavelet information gives an accuracy of 78%, trispectrum information gives an accuracy of 70%, combined morphological with wavelet information gives an accuracy of 77%, combined morphological with trispectral information gives an accuracy of 70%. By combining the evidence from both the morphological, wavelet and trispectrum features, an accuracy of 91% is obtained, indicating that ECG beat information is present in the hybrid features.

Cardiac Arrhythmia Classification Using Boosted Decision Trees

An intelligent system for the classification of Electrocardiograph (ECG) beat signal would play an important role in the diagnosis of cardiac arrhythmias. This paper employed a recently invented C5.0 decision trees (DTs) algorithm to develop a supervised ECG beat classifier. In general, decision tree algorithms have proved remarkable ability to derive meaning from complicated or imprecise data. Accordingly, they can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computational techniques. They are nonparametric methods with no assumptions about the space distribution and the classifier structure. This study investigated the performance of the C5.0 decision tree model with boosting on the diagnosis of ECG features' dataset. Boosting process significantly improves the accuracy of a C5.0 model. The algorithm builds up multiple decision-tree models in a sequential manner; the first model is built in the standard way. Then, each of the subsequent models focuses on the misclassified samples by the preceding model. Finally, new samples are classified by ensemble these models using a weighted voting procedure to combine the separate decisions into one overall choice. The objective of this work is to classify an ECG characteristic feature vector as either normal or arrhythmia. The classification performance of boosted C5.0 DTs is evaluated and compared to the one that achieved by multilayer feed-forward neural network. Experimental results showed that the boosted C5.0 DTs model has achieved a remarkable performance that reached 99% classification accuracy on both training and testing subsets.

Semisupervised ECG Ventricular Beat Classification With Novelty Detection Based on Switching Kalman Filters.

Automatic processing and accurate diagnosis of pathological electrocardiogram (ECG) signals remains a challenge. As long-term ECG recordings continue to increase in prevalence, driven partly by the ease of remote monitoring technology usage, the need to automate ECG analysis continues to grow. In previous studies, a model-based ECG filtering approach to ECG data from healthy subjects has been applied to facilitate accurate online filtering and analysis of physiological signals. We propose an extension of this approach, which models not only normal and ventricular heartbeats, but also morphologies not previously encountered. A switching Kalman filter approach is introduced to enable the automatic selection of the most likely mode (beat type), while simultaneously filtering the signal using appropriate prior knowledge. Novelty detection is also made possible by incorporating a third mode for the detection of unknown (not previously observed) morphologies, and denoted as X-factor. This new approach is compared to state-of-the-art techniques for the ventricular heartbeat classification in the MIT-BIH arrhythmia and Incart databases. F1 scores of 98.3% and 99.5% were found on each database, respectively, which are superior to other published algorithms' results reported on the same databases. Only 3% of all the beats were discarded as X-factor, and the majority of these beats contained high levels of noise. The proposed technique demonstrates accurate beat classification in the presence of previously unseen (and unlearned) morphologies and noise, and provides an automated method for morphological analysis of arbitrary (unknown) ECG leads.

Intelligent electrocardiogram pattern classification and recognition using low‐cost cardio‐care system

Electrocardiogram (ECG) contains detailed information regarding incidental abnormality of a subject. Manual analysis of a long time ECG record is a lengthy process. Computerised ECG analysis supports clinicians in decision making. While designing a low-cost diagnostic support system, constraints on the system resources limit the processing speed, eventually affecting the reliability. To resolve these issues, three key factors have been addressed in this study: the feature extraction method, total number of features and the database used. For feature extraction, ‘polar Teager energy’ algorithm has been developed, yielding nearly 70% saving in processing time as compared to other well-known methods. Using features with linear relationship leads to reduction in feature vector dimension, without compromising its classification performance. Therefore the linear relationship between two ECG features, namely ‘informational entropy’(S) and ‘mean Teager energy’ has been revealed. These features are utilised for ECG beat classification using ‘fuzzy C-means clustering’ algorithm. The algorithm is evaluated using the MIT-BIH database and then tested by ECG measured with the cardio-care unit. The QRS detection performance of the proposed method is very good, with 0.27% detection error rate. For classification of ECG beats, average sensitivity and positive prediction rate achieved are 98.93% each.

Automatic Classification of ECG Using Multi-Classifiers with a New Fusion Method

ECG signal contains abundant information of human heart activity. It is important basis of doctors’ diagnose. With the development of computer technology, computer aided analysis has been widely applied in the field of ECG analysis. Most of the traditional method is based on single classifier and too complex. Also, the accuracy is not high. This paper focuses on ECG heart beat classification, extracting different types of feature, training different classifiers by vector model and support vector machine (SVM), merging the result of multiple classifiers. In this paper, we used the advanced voting method (voting by weight) to fusion the result of different classifier, having compared it with the traditional voting method.It performed better than traditional method in term of accuracy

ECG Beats Classification Using Mixture of Features.

Classification of electrocardiogram (ECG) signals plays an important role in clinical diagnosis of heart disease. This paper proposes the design of an efficient system for classification of the normal beat (N), ventricular ectopic beat (V), supraventricular ectopic beat (S), fusion beat (F), and unknown beat (Q) using a mixture of features. In this paper, two different feature extraction methods are proposed for classification of ECG beats: (i) S-transform based features along with temporal features and (ii) mixture of ST and WT based features along with temporal features. The extracted feature set is independently classified using multilayer perceptron neural network (MLPNN). The performances are evaluated on several normal and abnormal ECG signals from 44 recordings of the MIT-BIH arrhythmia database. In this work, the performances of three feature extraction techniques with MLP-NN classifier are compared using five classes of ECG beat recommended by AAMI (Association for the Advancement of Medical Instrumentation) standards. The average sensitivity performances of the proposed feature extraction technique for N, S, F, V, and Q are 95.70%, 78.05%, 49.60%, 89.68%, and 33.89%, respectively. The experimental results demonstrate that the proposed feature extraction techniques show better performances compared to other existing features extraction techniques.

Patient-specific ECG beat classification technique.

Electrocardiogram (ECG) beat classification plays an important role in the timely diagnosis of the critical heart condition. An automated diagnostic system is proposed to classify five types of ECG classes, namely normal (N), ventricular ectopic beat (V), supra ventricular ectopic beat (S), fusion (F) and unknown (Q) as recommended by the Association for the Advancement of Medical Instrumentation (AAMI). The proposed method integrates the Stockwell transform (ST), a bacteria foraging optimisation (BFO) algorithm and a least mean square (LMS)-based multiclass support vector machine (SVM) classifier. The ST is utilised to extract the important morphological features which are concatenated with four timing features. The resultant combined feature vector is optimised by removing the redundant and irrelevant features using the BFO algorithm. The optimised feature vector is applied to the LMS-based multiclass SVM classifier for automated diagnosis. In the proposed technique, the LMS algorithm is used to modify the Lagrange multiplier, which in turn modifies the weight vector to minimise the classification error. The updated weights are used during the testing phase to classify ECG beats. The classification performances are evaluated using the MIT-BIH arrhythmia database. Average accuracy and sensitivity performances of the proposed system for V detection are 98.6% and 91.7%, respectively, and for S detections, 98.2% and 74.7%, respectively over the entire database. To generalise the capability, the classification performance is also evaluated using the St. Petersburg Institute of Cardiological Technics (INCART) database. The proposed technique performs better than other reported heartbeat techniques, with results suggesting better generalisation capability.

Effect of Multiscale PCA De-noising in ECG Beat Classification for Diagnosis of Cardiovascular Diseases

Current trends in clinical applications demand automation in electrocardiogram (ECG) signal processing and heart beat classification. This paper examines the design of an effective recognition meth...

Arrhythmia detection in single-lead ECG by combining beat and rhythm-level information.

In this paper, we propose a method for detecting arrhythmia in single-lead electro-cardiogram (ECG) signal. By applying a sequence of pre-processing steps (filtering, baseline correction), beat classification and rhythm identification, six different beat-types and four abnormal rhythms are detected. Beat classification uses fast Fourier transform (FFT) as the feature and a support vector machine (SVM) classifier. Subsequently rhythm identification uses a deterministic finite state machine to detect abnormal rhythms. We evaluate the performance of our technique on the MIT-BIH database, to obtain 97% beat classification accuracy and perfect rhythm identification result.

An adapting system for heartbeat classification minimising user input.

An adaptive system for the processing of the electrocardiogram (ECG) for the classification of heartbeats into beat classes that seeks to minimize the required input from the user is presented. A first set of beat annotations is produced by the system by processing an incoming recording with a global-classifier. The beat annotations are then ranked by a confidence measure calculated from the posterior probabilities estimates associated with each beat classification. An expert then validates and if necessary corrects a fraction of the least confident beats of the recording. The system then adapts by first training a local-classifier using the newly annotated beats and combines this with the global-classifier to produce an adapted classification system. The adapted system is then used to update beat annotations. Our results show that we can achieve a significant boost in classification performance of the system by using a small number of beats for adaptation.

Subband higher-order statistics and cross-correlation for heartbeat type recognition based on two-lead electrocardiogram.

Regular electrocardiogram beat classification system usually based on single lead ECG signal. This study designated to add a second lead of ECG signal to the system and apply higher-order statistics and inter-lead cross-correlation features to study the influence of the second lead to the recognition rates and noise-tolerance of the classifier. Discrete wavelet transformation is employed to decompose the ECG signals into different subband components and higher order statistics is recruited to characterize the ECG signals as an attempt to elevate the accuracy and noise-resistibility of heartbeat discrimination. A feed-forward back-propagation neural network (FFBNN) is employed as classifier. When compared with the system that uses only one lead, the second lead raises the recognition rate from 97.74% to 98.25%. We also study the ability of the two-lead system in resisting different levels of white Gaussian noise. More than 97.8% accuracy can be retained with the two-lead system even when the SNR decreases to 10 dB.

개인별 이상신호 검출과 QRS 패턴 변화에 따른 조기심실수축 분류

ABSTRACT Premature ventricular contraction(PVC) is the most common disease among arrhythmia and it may cause serious situations such as ventricular fibrillation and ventricular tachycardia. Nevertheless personalized difference of ECG signal exist, performance degradation occurs because of carrying out diagnosis by general classification rule. In other words, the design of algorithm that exactly detects abnormal signal and classifies PVC by analyzing the persons’s physical condition and/or environment and variable QRS pattern is needed. Thus, PVC classification by personalized abnormal signal detection and QRS pattern variability is presented in this paper. For this purpose, we detected R wave through the preprocessing method and subtractive operation method and selected abnormal signal sets. Also, we classified PVC in realtime through QS interval and R wave amplitude. The performance of abnormal beat detection and PVC classification is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate the average of 98.33% in abnormal beat classification error and 94.46% in PVC classification..