<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Meghriche, Salama</style></author><author><style face="normal" font="default" size="100%">Draa, Amer</style></author><author><style face="normal" font="default" size="100%">Boulemden, Mohammed</style></author><author><style face="normal" font="default" size="100%">Abada, Afnen</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Hybrid Neural Network for Detecting AtrioVentricular Block (AV B) in ECG Signal</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.446.1335&amp;rep=rep1&amp;type=pdf</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	Biomedical domains are characterized by the presence of large amounts of high dimensional data and a lack of general theories to understand structural and functional components. 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 work, we develop a method based on a hybrid ANN. This method uses three different feed forward type multilayer neural networks, with the ability to classify ECGs as normal or carrying atrioventricular block (AVB). A single output unit encodes the probability of AVB occurrences. A value between 0 and 0.05 being the desired output for a normal ECG, a value between 0.05 and 1 would infer an occurrence of an AVB. The results show that, this hybrid network has a good performance to detect AVBs. They show a sensibility of 98% and a specificity of 92%.
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