Asian Journal of Information Technology
ISSN: Online 1993-5994ISSN: Print 1682-3915
Abstract
Artificial Neural Networks (ANN) are computer-based expert systems that have proved to be useful in pattern recognition tasks. ANN can be used in different phases of the decision-making process, from classification to diagnostic procedures. In this research, we develop a method, based on a Compound Neural Network (CNN), to classify ECGs as normal or carrying an AtrioVentricular heart Block (AVB). This method uses three different feed forward multilayer neural networks. A single output unit encodes the probability of AVB occurrences. A value between 0 and 0.1 is the desired output for a normal ECG; a value between 0.1 and 1 would infer an occurrence of an AVB. The results show that the CNN has a good performance in detecting AVBs, with a sensitivity of 89% and a specificity of 86%.
How to cite this article:
Salama Meghriche , Amer Draa and Mohammed Boulemden . A High Performance CNN Architecture for the Detection of AVB Carrying ECGs.
DOI: https://doi.org/10.36478/ajit.2007.474.479
URL: https://www.makhillpublications.co/view-article/1682-3915/ajit.2007.474.479