Classificação de arritmias cardíacas através de uma estrutura competitiva de redes neurais convolucionais autoassociativas

Abstract

This work proposes a system for classifying cardiac arrhythmias based on a competitive structure of Autoassociative Convolutional Neural Networks. Three neural networks were trained to reconstruct Electrocardiogram (ECG) signals for cases of patients with supraventricular, ventricular and normal beats. After training, the networks were allocated in a competitive parallel structure for classification of arrhythmias. The MIT-BIH arrhythmia public database of ECG signals was used for training and testing the networks, and for each ECG signal, from each patient, the QRS complexes of the heartbeats were extracted, which were the characteristics used as input. for the system, and these signals, which were in the form of temporal signals (1D), were transformed into digital images (2D) in order to use the capacity of convolutional neural networks for pattern recognition and feature extraction in images. For the development and performance analysis of the proposed structure, two paradigms that have been used in works already presented in the literature were used: interpatient paradigm and intrapatient paradigm, and the system obtained an accuracy of 96.97%, sensitivity of 96.30% and precision of 93.59% for the intrapatient case and accuracy of 94.05%, sensitivity of 70.43% and precision of 65.74% for the interpatient case. A comparative analysis with results from arrhythmia classification systems already presented in the literature shows that the proposed system presented similar results or, in some cases, better results than those already obtained, thus showing the applicability of the proposed structure to the problem