Salama Meghriche  , Amer Draa  , Mohammed Boulemden  , 
    A High Performance CNN Architecture for the Detection of AVB Carrying ECGs,
    Asian Journal of Information Technology,
    Volume 6,Issue 4,
    2007,
    Pages 474-479,
    ISSN 1682-3915,
    ajit.2007.474.479,
    (https://makhillpublications.co/view-article.php?doi=ajit.2007.474.479)
    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%.
    Keywords: Artificial neural networks;biomedical data;Electrocardiogram (ECG);pattern recognition;signal processing