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Journal ArticleDOI

ECG beat recognition using fuzzy hybrid neural network

01 Nov 2001-IEEE Transactions on Biomedical Engineering (IEEE Trans Biomed Eng)-Vol. 48, Iss: 11, pp 1265-1271
TL;DR: The results of experiments of recognition of different types of beats on the basis of the ECG waveforms have confirmed good efficiency of the proposed solution and show that the method may find practical application in the recognition and classification of different type heart beats.
Abstract: Presents the application of the fuzzy neural network for electrocardiographic (ECG) beat recognition and classification. The new classification algorithm of the ECG beats, applying the fuzzy hybrid neural network and the features drawn from the higher order statistics has been proposed in the paper. The cumulants of the second, third, and fourth orders have been used for the feature selection. The hybrid fuzzy neural network applied in the solution consists of the fuzzy self-organizing subnetwork connected in cascade with the multilayer perceptron, working as the final classifier. The c-means and Gustafson-Kessel algorithms for the self-organization of the neural network have been applied. The results of experiments of recognition of different types of beats on the basis of the ECG waveforms have confirmed good efficiency of the proposed solution. The investigations show that the method may find practical application in the recognition and classification of different type heart beats.
Citations
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Journal ArticleDOI
TL;DR: A method for the automatic processing of the electrocardiogram (ECG) for the classification of heartbeats and results are an improvement on previously reported results for automated heartbeat classification systems.
Abstract: A method for the automatic processing of the electrocardiogram (ECG) for the classification of heartbeats is presented. The method allocates manually detected heartbeats to one of the five beat classes recommended by ANSI/AAMI EC57:1998 standard, i.e., normal beat, ventricular ectopic beat (VEB), supraventricular ectopic beat (SVEB), fusion of a normal and a VEB, or unknown beat type. Data was obtained from the 44 nonpacemaker recordings of the MIT-BIH arrhythmia database. The data was split into two datasets with each dataset containing approximately 50 000 beats from 22 recordings. The first dataset was used to select a classifier configuration from candidate configurations. Twelve configurations processing feature sets derived from two ECG leads were compared. Feature sets were based on ECG morphology, heartbeat intervals, and RR-intervals. All configurations adopted a statistical classifier model utilizing supervised learning. The second dataset was used to provide an independent performance assessment of the selected configuration. This assessment resulted in a sensitivity of 75.9%, a positive predictivity of 38.5%, and a false positive rate of 4.7% for the SVEB class. For the VEB class, the sensitivity was 77.7%, the positive predictivity was 81.9%, and the false positive rate was 1.2%. These results are an improvement on previously reported results for automated heartbeat classification systems.

1,449 citations


Cites background or methods from "ECG beat recognition using fuzzy hy..."

  • ..., [7], [9], [10]) and should be avoided....

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  • ...Classifiers methods employed include linear discriminants [7], back propagation neural networks [8]–[10], self-organizing maps with learning vector quantization [11], and self-organizing networks [12]....

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  • ...Features include ECG morphology [8], [9], heartbeat interval features [8]–[11], frequency-based features [7], higher order cumulant features [10], Karhunen–Loeve expansion of ECG morphology [11], and hermite polynomials [12]....

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Journal ArticleDOI
TL;DR: This work surveys the current state-of-the-art methods of ECG-based automated abnormalities heartbeat classification by presenting the ECG signal preprocessing, the heartbeat segmentation techniques, the feature description methods and the learning algorithms used.

635 citations


Cites background or methods from "ECG beat recognition using fuzzy hy..."

  • ...However, in [121,84,122,88], it was proposed a hybrid neuro-fuzzy network methods in order to minimize the problems of MLP, increasing its generalization and reducing its training time....

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  • ...Other techniques have also been employed, such as linear predictive coding [87], high order accumulates [88,89], clustering [84,90,91], correlation dimension and largest Lyapunov exponent [92,93], Hermite transform [94], local fractal dimension [95]....

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  • ...[88] S. Osowski, T.H. Linh, ECG beat recognition using fuzzy hybrid neural network, IEEE Trans....

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  • ...[138] 16 HBF, SOM clustering Acc = 98% Dokur and Olmez [98] 10 Fourier, Wavelet + FSDP MLP, RCE, Acc = 96% Novel hybrid NN Osowski and Linh [88] 6 HOSC fuzzy NN Acc = 96% Tsipouras et al....

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Journal ArticleDOI
TL;DR: Five types of beat classes of arrhythmia as recommended by Association for Advancement of Medical Instrumentation (AAMI) were analyzed and dimensionality reduced features were fed to the Support Vector Machine, neural network and probabilistic neural network (PNN) classifiers for automated diagnosis.

586 citations

Journal ArticleDOI
01 Sep 2008
TL;DR: A thorough experimental study to show the superiority of the generalization capability of the support vector machine (SVM) approach in the automatic classification of electrocardiogram (ECG) beats and suggest that further substantial improvements in terms of classification accuracy can be achieved by the proposed PSO-SVM classification system.
Abstract: The aim of this paper is twofold. First, we present a thorough experimental study to show the superiority of the generalization capability of the support vector machine (SVM) approach in the automatic classification of electrocardiogram (ECG) beats. Second, we propose a novel classification system based on particle swarm optimization (PSO) to improve the generalization performance of the SVM classifier. For this purpose, we have optimized the SVM classifier design 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 basis of ECG data from the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database to classify five kinds of abnormal waveforms and normal beats. In particular, they were organized so as to test the sensitivity of the SVM classifier and that of two reference classifiers used for comparison, i.e., the k-nearest neighbor (kNN) classifier and the radial basis function (RBF) 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 SVM approach as compared to traditional classifiers, and suggest that further substantial improvements in terms of classification accuracy can be achieved by the proposed PSO-SVM classification system. On an average, over three experiments making use of a different total number of training beats (250, 500, and 750, respectively), the PSO-SVM yielded an overall accuracy of 89.72% on 40438 test beats selected from 20 patient records against 85.98%, 83.70%, and 82.34% for the SVM, the kNN, and the RBF classifiers, respectively.

480 citations


Cites background or methods from "ECG beat recognition using fuzzy hy..."

  • ...Y. Bazi is with the College of Engineering, Al Jouf University, Al Jouf 2014, Saudi Arabia, (e-mail: yakoub.bazi@ju.edu.sa)....

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  • ...Note that, though we adopted the OAA strategy as a multiclass strategy, other strategies could also be considered, thanks to the general nature of the proposed PSO–SVM classification system....

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Journal ArticleDOI
TL;DR: The results of the performed numerical experiments for the recognition of 13 heart rhythm types on the basis of ECG waveforms confirmed the reliability and advantage of the proposed approach.
Abstract: This paper presents a new solution to the expert system for reliable heartbeat recognition. The recognition system uses the support vector machine (SVM) working in the classification mode. Two different preprocessing methods for generation of features are applied. One method involves the higher order statistics (HOS) while the second the Hermite characterization of QRS complex of the registered electrocardiogram (ECG) waveform. Combining the SVM network with these preprocessing methods yields two neural classifiers, which have been combined into one final expert system. The combination of classifiers utilizes the least mean square method to optimize the weights of the weighted voting integrating scheme. The results of the performed numerical experiments for the recognition of 13 heart rhythm types on the basis of ECG waveforms confirmed the reliability and advantage of the proposed approach.

473 citations


Cites background from "ECG beat recognition using fuzzy hy..."

  • ...classifier, the paper [6] has reported the mean error of 3....

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References
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Book
31 Jul 1981
TL;DR: Books, as a source that may involve the facts, opinion, literature, religion, and many others are the great friends to join with, becomes what you need to get.
Abstract: New updated! The latest book from a very famous author finally comes out. Book of pattern recognition with fuzzy objective function algorithms, as an amazing reference becomes what you need to get. What's for is this book? Are you still thinking for what the book is? Well, this is what you probably will get. You should have made proper choices for your better life. Book, as a source that may involve the facts, opinion, literature, religion, and many others are the great friends to join with.

15,662 citations

Journal ArticleDOI
TL;DR: A real-time algorithm that reliably recognizes QRS complexes based upon digital analyses of slope, amplitude, and width of ECG signals and automatically adjusts thresholds and parameters periodically to adapt to such ECG changes as QRS morphology and heart rate.
Abstract: We have developed a real-time algorithm for detection of the QRS complexes of ECG signals. It reliably recognizes QRS complexes based upon digital analyses of slope, amplitude, and width. A special digital bandpass filter reduces false detections caused by the various types of interference present in ECG signals. This filtering permits use of low thresholds, thereby increasing detection sensitivity. The algorithm automatically adjusts thresholds and parameters periodically to adapt to such ECG changes as QRS morphology and heart rate. For the standard 24 h MIT/BIH arrhythmia database, this algorithm correctly detects 99.3 percent of the QRS complexes.

6,686 citations

Proceedings ArticleDOI
01 Jan 1978
TL;DR: Experimental results are presented which indicate that more accurate clustering may be obtained by using fuzzy covariances, a natural approach to fuzzy clustering.
Abstract: A class of fuzzy ISODATA clustering algorithms has been developed previously which includes fuzzy means. This class of algorithms is generalized to include fuzzy covariances. The resulting algorithm closely resembles maximum likelihood estimation of mixture densities. It is argued that use of fuzzy covariances is a natural approach to fuzzy clustering. Experimental results are presented which indicate that more accurate clustering may be obtained by using fuzzy covariances.

1,988 citations


"ECG beat recognition using fuzzy hy..." refers background or methods in this paper

  • ...The matrix is defined as follows [9], [ 12 ]:...

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  • ...The G‐K algorithm can be presented in the following way [ 12 ], [13]....

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  • ...One of the most efficient fuzzy clustering algorithms, able to take into account different shapes of the clusters, is the Gustafson-Kessel (G‐K) algorithm, extending the c-means algorithm [10], [11] by using scaled metric norm for the distance [ 12 ], [13]...

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Book
09 Jul 1999
TL;DR: This paper presents a meta-modelling framework that automates the very labor-intensive and therefore time-heavy and therefore expensive process of rule generation and estimation in the context of cluster dynamics.
Abstract: Introduction. Basic Concepts. Classical Fuzzy Clustering Algorithms. Linear and Ellipsoidal Prototypes Shell Prototypes. Polygonal Object Boundaries. Cluster Estimation Models. Cluster Validity. Rule Generation with Clustering. Appendix. Bibliography.

925 citations

Journal ArticleDOI
TL;DR: FCA better predicts future cardiac events in patients with positive stress tests than the ST-segment alone, which combined with its usefulness in predicting the extent of coronary disease provides the basis of a clinical strategy for managing patients withpositive stress tests.
Abstract: Several studies have shown that combining the change in the ST-segment with another exercise variable improves the predictive value of stress testing. However, no method has been able to combine many stress test variables with the ST-segment change simultaneously and help the clinician better predict future cardiac events. Fuzzy Cluster Analysis (FCA) was used to combine 5 stress test variables with ST-segment deviation to classify each of 232 positive outpatient stress tests as mildly, moderately, or severely abnormal. Cardiac events were recorded in these 3 patient groups up to 96 months (mean 65 months) after the stress tests. Coronary angiography was performed on 159 of these patients within 1 month of their stress tests. FCA better separated the 3 event-free survival curves than classifying the stress tests by three ST-segment (0.5-1.5 mm, 2-2.5 mm, > 3 mm) groups (p < 0.05). At 2 years, 90% of the FCA mild group were compared with 70% for the 0.5-1.5 mm group (p < 0.01). Moderate and severe tests by FCA separated patients with an intermediate from those with a poor prognosis while the 2-2.5 mm and 3 mm or more ST-segment curves did not (p < 0.05). FCA showed overall better correlation with coronary score (r = 0.71) than did the graded ST-segment groups (r = 0.48). FCA predicted both mild and high-grade (triple-vessel and left main) coronary disease better than ST-segment alone. Thus FCA better predicts future cardiac events in patients with positive stress tests than the ST-segment alone. This combined with its usefulness in predicting the extent of coronary disease provides the basis of a clinical strategy for managing patients with positive stress tests.

737 citations