@article{MAKHILLAJIT2007645362,
    title = {A High Performance CNN Architecture for the Detection of AVB Carrying ECGs},
    journal = {Asian Journal of Information Technology},
    volume = {6},
    number = {4},
    pages = {474-479},
    year = {2007},
    issn = {1682-3915},
    doi = {ajit.2007.474.479},
    url = {https://makhillpublications.co/view-article.php?issn=1682-3915&doi=ajit.2007.474.479},
    author = {Salama Meghriche,Amer Draa and},
    keywords = {Artificial neural networks,biomedical data,Electrocardiogram (ECG),pattern recognition,signal processing},
    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%.}
    }