Fractals everywhere
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
SBRN '02 Proceedings of the VII Brazilian Symposium on Neural Networks (SBRN'02)
Advanced Methods And Tools for ECG Data Analysis
Advanced Methods And Tools for ECG Data Analysis
Adaptive wavelet network for multiple cardiac arrhythmias recognition
Expert Systems with Applications: An International Journal
Adaptive neuro-fuzzy inference system for classification of ECG signals using Lyapunov exponents
Computer Methods and Programs in Biomedicine
Nonlinear address maps in a one-dimensional fractal model
IEEE Transactions on Signal Processing
Using iterated function systems to model discrete sequences
IEEE Transactions on Signal Processing
Wavelet neural networks for function learning
IEEE Transactions on Signal Processing
Using wavelet network in nonparametric estimation
IEEE Transactions on Neural Networks
MART: a multichannel ART-based neural network
IEEE Transactions on Neural Networks
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The electrocardiogram (ECG) signal is widely employed as one of the most important tools in clinical practice in order to assess the cardiac status of patients. The classification of the ECG into different pathologic disease categories is a complex pattern recognition task. In this paper, we propose a method for ECG heartbeat pattern recognition using wavelet neural network (WNN). To achieve this objective, an algorithm for QRS detection is first implemented, then a WNN Classifier is developed. The experimental results obtained by testing the proposed approach on ECG data from the MIT-BIH arrhythmia database demonstrate the efficiency of such an approach when compared with other methods existing in the literature.