TY  - JOUR
T1  - A High Performance CNN Architecture for the Detection of AVB Carrying ECGs
AU - , Salama Meghriche AU - , Amer Draa AU - , Mohammed Boulemden 
JO  - Asian Journal of Information Technology
VL  - 6
IS  - 4
SP  - 474
EP  - 479
PY  - 2007
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2007.474.479
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2007.474.479
KW  - Artificial neural networks
KW  -biomedical data
KW  -Electrocardiogram (ECG)
KW  -pattern recognition
KW  -signal processing
AB  - 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%.
ER  -