A Competitive Structure of Convolutional Autoencoder Networks for Electrocardiogram Signals Classification

• Alexandre Farias Baia UFPA
• Adriana Rosa Garcez Castro UFPA

DOI: https://doi.org/10.5753/eniac.2018.4446

Resumo

This paper presents the proposal of an electrocardiogram (ECG) signals classification system through a competitive structure of Convolutional Autoencoders (CAE). Two Convolutional Autoencoders were trained to reconstruct ECG signals for the cases of patients with arrhythmia and patients with signals considered normals. After the training, the two networks were arranged in a competitive parallel structure to classify these signals. For the development and testing of the system, the MIT-BIH Arrhythmia Database of ECG signals was used. An accuracy of 88,9% was achieved considering the database used for system testing.

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