Bioinformatics: the machine learning approach
Bioinformatics: the machine learning approach
Neural Networks and Artificial Intelligence for Biomedical Engineering
Neural Networks and Artificial Intelligence for Biomedical Engineering
IEEE Transactions on Information Technology in Biomedicine
Artificial Intelligence in Medicine
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The purpose of the paper is to present two networks. These networks were fed with same measurements from one lead of the electrocardiogram (ECG) but in different architectures. The first one based on a compound neural network (CNN) composed of three different multilayer neural networks of the feed forward type, and the second one based on only a multi-layer perceptron (MLP). Such both of them have the capability to classify ECGs as carrying atrioventricular blocks (AVB) or not. For each test case in the test set the neural networks classifier present an output value between 0 and 1. A threshold in this interval was used above which all values were regarded as consistent with AVB. The difference in performance between the two neural networks classifiers was measured as the difference in area under the receiver operating characteristic (ROC) curves.