Artificial neural networks for automatic ECG analysis
IEEE Transactions on Signal Processing
Expert Systems with Applications: An International Journal
Fractal QRS-complexes pattern recognition for imperative cardiac arrhythmias
Digital Signal Processing
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Robust multiple cardiac arrhythmia detection through bispectrum analysis
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Feature extraction for ECG heartbeats using higher order statistics of WPD coefficients
Computer Methods and Programs in Biomedicine
ICMB'10 Proceedings of the Second international conference on Medical Biometrics
Automatic Classification of Heartbeats Using Wavelet Neural Network
Journal of Medical Systems
ECG arrhythmia classification based on optimum-path forest
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
This paper proposes a method for electrocardiogram (ECG) heartbeat detection and recognition using adaptive wavelet network (AWN). The ECG beat recognition can be divided into a sequence of stages, starting with feature extraction from QRS complexes, and then according to characteristic features to identify the cardiac arrhythmias including the supraventricular ectopic beat, bundle branch ectopic beat, and ventricular ectopic beat. The method of ECG beats is a two-subnetwork architecture, Morlet wavelets are used to enhance the features from each heartbeat, and probabilistic neural network (PNN) performs the recognition tasks. The AWN method is used for application in a dynamic environment, with add-in and delete-off features using automatic target adjustment and parameter tuning. The experimental results used from the MIT-BIH arrhythmia database demonstrate the efficiency of the proposed non-invasive method. Compared with conventional multi-layer neural networks, the test results also show accurate discrimination, fast learning, good adaptability, and faster processing time for detection.