Open AccessECG Classification Using Artificial Neural Networks
Author(s) -
F A Rivera Sánchez,
J A González Cervera
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1221/1/012062
Subject(s) - convolutional neural network , artificial neural network , computer science , artificial intelligence , pattern recognition (psychology) , data set , set (abstract data type) , feedforward neural network , time delay neural network , deep learning , machine learning , programming language
We propose two Artificial Neural Networks (ANN) architectures for classification of electrocardiogram (ECG) signals to compare effectiveness between them. The atrial fibrillation (AF) classification data set provided by PhysioNet/CinC Challenge 2017 was used. The ANNs proposed are a feed forward neural network (FFNN) and a convolutional neural network (CNN). In order to train the convolutional neural network we transformed the ECG signals to images. The convolutional neural network overcomes the other by reaching an average accuracy of 97.6% in prediction set.