Journal ArticleDOI
On-line heart beat recognition using Hermite polynomials and neuro-fuzzy network
TLDR
A neuro-fuzzy approach to the recognition and classification of heart rhythms on the basis of ECG waveforms using a fuzzy neural network based on the Hermite characterization of the QRS complexes.Abstract:
This paper presents a neuro-fuzzy approach to the recognition and classification of heart rhythms on the basis of ECG waveforms. The important part in recognition fulfills the Hermite characterization of the QRS complexes. The Hermite coefficients serve as the features of the process. These features are applied to a fuzzy neural network for recognition. The results of numerical experiments have confirmed very good performance of such a solution.read more
Citations
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Journal ArticleDOI
ECG beat classification using PCA, LDA, ICA and Discrete Wavelet Transform
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.
Journal ArticleDOI
Classification of Electrocardiogram Signals With Support Vector Machines and Particle Swarm Optimization
Farid Melgani,Yakoub Bazi +1 more
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.
Journal ArticleDOI
Block-Based Neural Networks for Personalized ECG Signal Classification
Wei Jiang,G. Seong Kong +1 more
TL;DR: Simulation results using the Massachusetts Institute of Technology/Beth Israel Hospital (MIT-BIH) arrhythmia database demonstrate high average detection accuracies of ventricular ectopic beats and supraventricular ectopy beats patterns for heartbeat monitoring, being a significant improvement over previously reported electrocardiogram (ECG) classification results.
Journal ArticleDOI
A driver fatigue recognition model based on information fusion and dynamic Bayesian network
TL;DR: The experimental validation shows the effectiveness of the proposed driver fatigue recognition model and indicates that the contact physiological features are significant factors for inferring the fatigue state of a driver.
Journal ArticleDOI
Application of Cross Wavelet Transform for ECG Pattern Analysis and Classification
TL;DR: The proposed algorithm analyzes ECG data utilizing XWT and explores the resulting spectral differences and heuristically determined mathematical formula extracts the parameter(s) from the WCS and WCOH that are relevant for classification of normal and abnormal cardiac patterns.
References
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Proceedings ArticleDOI
Fuzzy clustering with a fuzzy covariance matrix
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.
Journal ArticleDOI
A patient-adaptable ECG beat classifier using a mixture of experts approach
TL;DR: A "mixture-of-experts" (MOE) approach to develop customized electrocardiogram (EGG) beat classifier in an effort to further improve the performance of ECG processing and to offer individualized health care.
Journal ArticleDOI
Clustering ECG complexes using Hermite functions and self-organizing maps
TL;DR: An integrated method for clustering of QRS complexes is presented which includes basis function representation and self-organizing neural networks (NN's) and outperforms both a published supervised learning method as well as a conventional template cross-correlation clustering method.
Journal ArticleDOI
ECG beat recognition using fuzzy hybrid neural network
Stanislaw Osowski,T.H. Linh +1 more
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.
Journal ArticleDOI
Real-time discrimination of ventricular tachyarrhythmia with Fourier-transform neural network
TL;DR: The authors' method to discriminate life-threatening ventricular arrhythmias by observing the QRS complex of the electrocardiogram (ECG) in each heartbeat achieved high sensitivity and specificity in discrimination of supraventricular rhythms from ventricular ones.