Automatic Detection of Arrhythmias Using Wavelets and Self-Organized Artificial Neural Networks

The arrhythmias or abnormal rhythms of the heart are common cardiac riots and may cause serious risks to the life of people, being one of the main causes on deaths. These deaths could be avoided if a previous monitoring of these arrhythmias were carried out, using the Electrocardiogram (ECG) exam. The continuous monitoring and the automatic detection of arrhythmias of the heart may help specialists to perform a faster diagnostic. The main contribution of this work is to show that self-organized artificial neural networks (ANNs), as the ART2, can be applied in arrhythmias automatic detection, working with Wavelet transforms for feature extraction. The self-organized ANN allows, at any time, the inclusion of other groups of arrhythmias, without the need of a new complete training phase. The paper presents the results of practical experimentations.

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